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https://media.trustradius.com/product-logos/Uv/Xp/77N37PEPH17Z-180x180.PNGIBM Watson Studio: Ideal for Rapid Data Science and ML POCs and Deployments with WatsonWatson Studio is the first third of IBM's new Watson machine learning data pipeline. It's a powerful, reasonably intuitive, low-code environment for building machine learning models and integrating IBM's machine learning APIs (speech recognition, image recognition, etc.) into your ML pipeline. If you already consume Watson APIs, Watson Studio will help streamline current and future deployments.,Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc. SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly. Enforced best-practices set up POCs for deployment in production with a minimum of re-work. Estimator validation lets data scientists test and prove different models.,Watson Studio's UI is not always intuitive, especially when it comes to requirements and specific settings. Documentation is not strong; tutorials and walkthroughs are noticeably light. Tight integration with IBM APIs also means less well-made integrations to third party data sources and APIs—MySQL support notably absent.,10,As a reseller, selling Watson Studio as a machine learning platform package is relatively straightforward. Buyers and partners know, appreciate, and trust the IBM brand. Watson Studio models obey best practices, which means they are less subject to human error.,Data Refiner is the one part of the portfolio that is the most lacking. It's poorly designed and poorly thought out, and requires far more time to use and administer than the benefits it delivers. In theory, it should make the data science experience better, but it's cumbersome and shockingly dumb—loading a CSV of integer values, for example, requires manual recasting of every column as a numeric value. It can't detect that.,We use R Studio in addition to Watson Studio. We typically do POCs in R and then upscale to Watson Studio for production, because the IBM infrastructure means great governance and compliance and a minimum of system administration after deployment. With the new OpenScale ML management, it will also provide more transparency in models.,Splunk Enterprise and RapidMiner Studio,Tableau Desktop, Alteryx Analytics, KNIME Analytics PlatformIBM Watson studios , our first stepsIt is currently used by the data analytics department. It is used to address cost analysis and actuarial analysis.-,It is relatively easy to use It works seamlessly with multiple languages. Its administration is surprisingly easy And it's easy to install / upgrade / maintain,Need better training materials for data scientists. Especially the ones who are not formally educated as data scientists. The videos in the tutorials are all on Youtube which are usually blocked on most work campuses. And the IBM Think campus training could have been better as well,8,We are hoping that our current analytics team's analysis process timing improves. We hope to understand our member better We hope to use this to add more services to our clients,Yes, we have been able to save time by connecting to IBM governance catalogs,We got it as part of our package with IBM Integrated Analytics systems. Additionally, since we are predominantly an IBM shop this was an easy decision.,,IBM InfoSphere DataStage, IBM InfoSphere QualityStage, IBM InfoSphere Information ServerWatson Studio reviewWe currently use Watson Studio for NLP/NLU purposes in our client environments. It addressed problems in physical space interactions between front line associates and customers. We also leverage tonality and sentiment models in addition to transcription to effectively process large amounts of voice interactions in the physical world. Watson Studio has proven useful in addressing these problems in these environments, but we are limited in our capacity to roll it out further due to edge compute limitations with the platform.,Speech to text Keyword analysis Tonality Sentiment,Architectural support team to get up and running,5,Very difficult to tie Watson to ROI in our environment. Negative experience.,No,We [chose Watson Studio because we] received investment from an ex-IBMer,IBM Cloud Foundry,Slack, Azure SQL DatabaseGreat first impression and lot of opportunities with Watson studioWe have rules based applications that help airlines make real time decisions. We are to embed Watson studio to use historical data and optimize the outcomes of those decisions.,Intuitive GUI for us to begin using the studio It works well that we can embed the decisions into our existing offering without a lot of changes The pricing model is flexible Like the opportunity to embed more data,Provide hint to use services based on verticals Suggest how it could be embedded into mobile apps Would like to understand the deployment model better,10,Could instantly show data driven insights to drive 20% incremental revenue over existing results Still don't have a real use case for unstructured data like twitter feed Some of the insights around user actions have driven new projects to automate mundane tasks,Still to explore this feature,Demonstrated success by airlines have been our primary motivation behind using Watson over others. We are still open to other solutions but Watson seems to have a lot of promise for the travel vertical,TensorFlow and Amazon SageMaker,IBM Cloud IaaS (formerly IBM Bluemix - IaaS), IBM Cognos, Microsoft Power BIWatson Studio makes product development easyWe use it to create products to detect diseases from skin images so that the doctors can monitor the progress of the diseases and deliver the best therapy. It is being used by the whole organisation as we are a medical AI company and the use of AI is integral to our product.,It is easy to use and I don't need to have a team full of data scientists to use them It is easy to deploy when the models are trained and we don't need to hire many software engineers to take care of deployment It allows us to test different models rapidly and so helps to accelerate the product development process,The cost is steep and so only companies with resources can afford it It will be nice to have Chinese versions so that Chinese engineers can also use it easily It takes a while to learn how to input different kinds of skin defects for detection,8,It allows rapid product development so that we're able to test concepts and ideas quickly The cost of ownership is high for a small company like ours, and so we talked to a lot of alternatives before deciding I can use software engineers who are not data scientists to develop applications, and this saves some money,It is useful to some extent because it allows the team of engineers to access the data from multiple sources and enable collaboration. The shaping operation that allows us to curate and organize the data is also useful from the productivity point of view. The data visualization is also useful to allow us to see where are data gaps so that we can do experiments to fill them.,One advantage is that Watson Studio uses a lot of open source solutions and brings together numerous popular tools for data science on a single platform. For example, we use a lot of Python and R, and Watson Studio's single platform brings convenience when we're testing different libraries. Other tools are less comprehensive compared to Watson Studio.,Microsoft Visual Studio Code, Microsoft 365 Business, Microsoft VisioAnalytics for the MassesWe are working to leverage data analytics using an on-premises deployment to aid in predicting faults for our customers in a proactive/reactive manner. We are looking to leverage efficient and regularly trained algorithms in our Diagnostics Engine/BPMS to reduce our overall time to handle and potentially eliminate tickets opened by our customers,Ease of use and quick to explore Guided experiences and ability to leverage multiple algorithms to identify the best one Great support and sales teams,There isn’t much I think I can provide critical or improvement feedback on,10,I think WS is going to allow more casual BA personnel to provide advanced insights/api’s to the business without the requirement of dedicated traditional data scientist skills with strong statistical/modeling background I love the approach to bring Analytics to the masses.,We have to used that portion yet,Ease of use, and ability to allow business analysts to use the tool without special software code training that is required in Jupyter. We are strong advocates of Jupyter advocates.,Alteryx Analytics Gallery,Alteryx AnalyticsWatson Studio - Data Science PlatformWatson Studio is a wonderful product. Currently, I am a SPSS Modeler user and we are looking at migrating to Watson Studio Local. Watson Studio offers a great development and deployment platform for data scientist.,Flexibility in the use of different data science development environment, e.g. R, Python and SPSS modeler Deployment capability in miroservices,Nothing at the moment,9,Unified platform Ease of deployment,Not utilize this feature yet,This platform helps to facilitate the broad user base. For example, it supports everyone from hard-core data scientists, who do in-depth coding, to citizen data scientists; even all the way to business analysts.,IBM SPSS ModelerThe Most Robust AvailableMy organization is currently going thru an expansion process, from being solely a consulting company to creating and developing products itself and for this expansion, a new team was assembled, the Research & Development team and I'm part of this team. As part of our research to create products that are really relevant to the market we are aiming at, and since we're already are a gold level IBM partner organization, it made sense for us to acquire knowledge with IBM DSx in order to maximize our efficiency and develop better products.,Feature rich. IBM DSx provides a plethora of tools to leverage the use of data science in your organization and suit your specific needs. IBM DSx supports a huge variety of sources of data. From your traditional SQL database to every major data warehouse, IBM DSx does a great job at connecting to or pulling from your data source. Its greatest strength is the fact that is a cloud-based service. There's no need to waste time on configuring and maintaining an environment to start analyzing data, which may not be an easy task.,Pricing. The price for this product is quite steep and, since it features so many solutions, it makes sense to cost as much as it does. But the creation of personal plans with fewer features might prove interesting to bring the product to a broader audience, like enthusiasts that are starting to get in touch with data science. Some issues regarding notebooks and the use of data refinery are quite annoying to the experience because, depending on the use that you make of it, they might appear quite regularly. Lack of a changelog. Like many IBM products and platforms, DSx is in constant development and is updated regularly. This is a great point, except for the fact that sometimes it lacks a changelog to properly inform what has been changed, requiring the user to investigate on its own.,8,Since we've used IBM DSx mainly while investigating tools to be able to create better products, we've yet to fully use its features and in a real-world scenario. For this particular reason, we haven't been able to determine a proper ROI for this particular product.,Yes, I was able to save a lot of time regarding connecting to my data sources and refining, mainly on the connections camp. The fact that the data refinery required the data to be presented in a certain way mostly is what kept me from achieving a great time saving on this aspect of the product. Still, the huge catalog, ease of use and pre-configured environment made a huge impact on speeding up the analysis process.,Points like the ease of use, a great number of tutorials available and the number of features are what attracted us to IBM DSx. Since my organization is already an IBM gold partner, it made sense to use a product from a catalog that we already were extremely familiar with rather than training ourselves and spend a great amount of time into implementing other tools.,IBM Watson Analytics, IBM Cloud PaaS (formerly IBM Bluemix - PaaS), IBM MaximoWatson vs. DATAWatson Studio is used within Manifest Life Inc to help gather, process, and visualize various amounts of data. We use Watson Studio throughout our data management lifecycle and view it as the best all-in-one data management offering within the market. Watson Studio has helped our organization manage our data operations more fluidly through the offerings ease of use and minimal learning barrier. This product from IBM works like a charm and is flexible in small and/or large business environments. Data sets of all size can be easily cleaned, processed and leveraged in order to help meet our business objectives and create new opportunities.,Cloud-based file sharing helped our organization stay up to date when managing assets, new or old. Watson studio does a fantastic job visualizing outcome data which enabled our organization to easily create a narrative based on what we were able to see. Particularly within our organization, Watson Studio strength was noticed in its ability to processes enormous amounts of data in such a short amount of time.,Watson Studio could used improvement in its user-based community. I'd like to see more local and remote events showcasing its potential. Watson Studio could improve by providing its users, use-cases that leverage data in unusual ways. We think Watson studio could also improve by decreasing its price in order to capture new talent in the data industry.,10,Watson Studio has allowed our organization leverage open data to create new streams of revenue that previously could not be tapped into. Watson Studio has allowed us to conduct business without the need of additional third party vendors. Watson Studio has allowed us to see a ROI where previously there was none.,Time savings is one of Watson Studio's greatest perks. Time is money. Our organization was able to collect, clean, and analyze data through one platform, ultimately saving the organization time by reducing the amount of time spent navigating singular third party offerings.,We chose Watson Studio because of its robustness, community, and the quality of documentation supporting its offering.,RStudio, Scala, Apache Spark and TensorFlow,Watson Studio (formerly IBM Data Science Experience), Watson Knowledge Catalog, IBM Cloud PaaS (formerly IBM Bluemix - PaaS), IBM Cloud IaaS (formerly IBM Bluemix - IaaS),Data cleansing Data visualizing Data sharing,Understanding ML feature Using algorithms Features including spark,10I used to swear by IBM DSXI used to teach with Watson Studio, but not anymore. It is inconsistent in many ways it breaks and when I ask for help, no one seems to be bothered. When I do get a response, it doesn't solve my problems. IBM needs to get it's act together or risk losing out.,Good UI Easy of use Incorporates both Python and R,Could be more holistic Have a look at Microsoft Studio, it's well integrated Not very well thought out,1,No coding data science for my students Good alternative platform to MS studio ML Good beginner's first contact with data science,I don't use these as my datasets are already on hand.,Hong Kong University was teaching non-technical students data science. We've looked at a couple of platforms and found that DSX had a couple of Jupyter NBs to start and it was a minimal effort on the part of Lecturers to produce a lesson plan. However, the erratic nature of Apache Spark in R was a big letdown.,,AWS Lambda, Microsoft Azure, Tableau DesktopIBM Data Science Experience (DSx) for big data analysisWe use IBM data science experience in order to create and train predictive analytics model: we implement some use cases about this topic, using different models. We've focused on model referring to vehicle registration using historical models and predictive variables like oil trend. Also, we use Jupyter Notebooks to analyze Twitter data and create data visualizations.,You can use SPSS model in order to predict trend with historical data You can use R in order to clean up your data a Jupyter notebook You can use Jupyter Notebooks to analyze Twitter data and create data visualizations,We try to install DSX in the local environment but it needs more resources I'd like a better visualization library for charts I'd like more webinar in order to introduce to the platform, also in Italian language,8,I work with this product only for experimental works, I can't answer to this question. I hope that this product has the ability to proactively and repeatedly reduce costs and increase productivity. I suppose that this platform could improve decision making and customer service,I achieved these time savings using a non-local installation because I haven't spent time in installation and configuration. Using excel import file features and the import from code implemented with SPSS I am saving a lot of time. Besides, data preparation services and as scheduling tasks are available in order to save hours,,IBM DSx has a lot of advantages that allow you to start a new project with low cost, without installation, and time savings, you could start immediately both with a new project then import your old project implemented with SPSS or R. The community is very responsive for advice and help in troubleshooting.,Hortonworks Data Platform, Cloudera Data Science Workbench and RapidMiner Studio,Automation Anywhere, Hortonworks Data Platform, Oracle WebCenter PortalBeginner developer for DSxIt was being used to analyze big data. It was being used in our department for development purposes, and testing its limits and capabilities. The software was still in development when I was working on it, and my job was to create intro level content displaying the different types of services the platform offered.,Exploratory data analysis Concurrent project and data management Built-in libraries and frameworks for different programming languages,The data limit was low, but it depends on how much you pay,9,Fast analysis Easy to access Data management,Yes.,I chose DSx because I was working for a company which was developing DSx and they needed content to showcase its abilities.,RStudio,ArcGISIBM DSx, soon to be even more productive as Watson StudioI use DSx both for internal projects as well as for clients. Depending on the situation I'll sometimes use DSx desktop and the cloud service other times. Most of my applications represent some form of analytical pipeline and are used for experimentation and/or development rather than any kind of production use.,DSx is particularly well suited for ML data prep. It's easy to ingest from many different kinds of sources and then perform various cleansing, transformation, and enrichment operations. DSx makes collaboration with other team members very easy. Control over who can see and interact with each project is straightforward and simple to administer. DSx doesn't create proprietary lock-in. Notebooks can be exported in a number of different forms to share people that don't use DSx or to run in a different environment.,Stability has gradually improved over the past year or so, but could still be better. I'd like to see options for leveraging a GPU on the cloud-hosted version. I'd like to see even more ML model lifecycle support, but it's my understanding that this is coming with the move to Watson Studio.,8,DSx has made it easier to collaborate on multiple projects with colleagues It's very convenient for quick hit applications as an alternative to spinning up local containers.,Most of the projects for which I've used DSx have tended to be "one-off's" and so the need and value of the data catalog has been relatively low. However, it's a particularly nice feature when sharing projects with others and for reproducibility.,I was a heavy user of Jupyter notebooks both locally and on a server using JupyterHub. Although I still use that option occasionally, the convenience and added features of DSx (and Watson Studio moving forward) are an attractive draw.,,IBM Streams, Apache Kafka, IBM Cloud Object StoragePretty good, but still away from perfectWe have 10 users. It is used by the analytics team and by some other citizen data scientists capable of making use of it. We mainly use it for problems big enough that we are not able to tackle in our on-premise servers.,Integration with Spark Jupyter-like environment Asset and community access,Easier access to data Connection with on-premise datasources Personalization,9,We managed (marginally) to succeed. It was difficult to configure; it was not known which ports to open in our firewall and some other issues.,We were testing it. Our conclusion is that it is better than Jupyter but less configurable (e.g. nbextensions). Taking into account the future of Flows, we would also need to compare it with KNIME.,KNIME Analytics Platform, Anaconda, RStudioMy thoughts on DSXWe were using DSx for cleansing and modeling data for the insurance industry. DAD has been used for our small data science team only.,To have Jupyter notebooks and RStudio in the same environment is great! The free Spark engine is perfect and enough to support the development activities. The integration with GitHub facilitated our collaborative work.,To set up a new Spark cluster and use it with DSx is a bit hard. It would be great to have the option to create a new big cluster without leaving DSx. I've faced several problems with DSx desktop.,9,Definitely DSx has helped us to develop our data science skills and achieve our objectives. As we were using the free tier, it's difficult to assess our ROI.,I haven't had time to investigate these tools, but certainly would love to get my hands on them. If there are training videos available, that would be great.,Because in DSx ask is ready to use. I spent 3 minutes creating my IBM account and was ready to go. As a Data Scientist, it is not my objective to learn about how to set up tools and look after infrastructure; we need to use tools to help us focus on our main activities. DSx is perfect for that.,Forget the configuration. Use DSx.We use DSX in the development department. Previously, we spent a lot of time configuring our machines, setting up the computers and the virtual environments, getting the libraries. We can show our work to our boss without going with our computers. WIth DSX, we are able to do our work in the platform and share it with whoever we want. Then, they can leave comments in the notebook and we can review them later.,Configuration: You can forget about all the setup. You just open a notebook, import the libraries you want and start writing Sharing: You can share your thoughts with anyone, because all your code lives in the cloud Tools: IBM has amazing tools for speech recognition, image processing, and so on,Because of my use, I didn't find anything to improve. I think that making things more visual will be useful for non-expert people, like flowcharts for example,9,Time saving: As I said previously, I got a model in less than two weeks,I didn't use any of those features. I worked with less than 60k entries, so, at least for my task, I just needed to update the file and start processing it. In the future, probably I will need to work with more than 1M entries, so it will be useful to use those features.,IBM Data Science Experience integrate a lot of features in one place. I needed a lot of feedback from our clients, and share with them our advances. That would not have been possible using other open source tools. Also, you can share your notebooks with expert people publishing them in the platform.,,IBM Cloud IaaS (formerly IBM Bluemix - IaaS), IBM Watson AnalyticsWatson Studio opinionAs an IBM partner, my company is evaluating this product for potential use for our clients. Usually we offer data mining projects in order to meet specifics needs our clients demand. As a example we develop models for risk prediction across many financial organizations.,I like very much to have a multitude of tools integrated nicely in one place. The documentation is ever-growing and a source of continuous new insights. I appreciate the notable curatorial effort. The tools and services are ever-growing too. The focus on open-source tools and the "no barriers in the middle" is really one of the strongest points in my opinion.,Well, I had issues of performance and responsivity. As a tool for exploring new ideas and learning new techniques the free plan is limited to very small datasets in order to perform in a reasonable time. As an example a simple test of Random Forest on a dataset of 100k rows didn't finish in the Rstudio provided by Watson whereas it took half an hour in the RStudio installed in my desktop.,6,We didn't measure that yet.,As far as I know the Data Refinery is not capable of good feature engineering, which is the most consuming task in our work.,Well, it helped very much with the integration with Spark and I'm aware of the utility of Watson services.,IBM SPSS Modeler, RStudio, KNIME Analytics Platform and Microsoft Azure Machine Learning Workbench,IBM SPSS Modeler, RStudioDSx ReviewIt is used to create demos of the tool and real cases for selling it. In my organization it is not used.,The run period is good and what you run is saved. You have to install nothing and that's a great advantage. You can create conections to save your files in the cloud. It works perfectly in a collaborative environment and it's easy.,It is limited still. Jupyter supports many programming languages but DSx just manages three. The scheduled jobs are fantastic but it can be more frequent. The multiple errors of the account, the group, the services are confusing.,10,So many things are free in DSX so it worth completly. If you need more space, more RAM or something which you have to pay, it is good here because of the optimization. When you have more products of IBM, the return of investment is bigger because of the synergy.,I haven't experienced with those features but I know what Data Refinery does, and I prefer to do it by code.,I think because of the colaborative environment, the cloud stuff and the scheduled jobs are fascinating,,IBM Watson AnalyticsMarket Simulator with DSXDSX is been used by my department only as a pilot project to prove the tool capabilities, although we are currently doing different projects: one to simulate the Brazilian retail market to find the best places to open stores, measure performance of existing stores, another to read verbatim text from our clients, and a third to correlate sales behavior with population cluster.,Flexibility: all the other solutions we procure were modular with black boxes. Data center is in Europe, since in my company we can't have data in US From all the solutions we procure IBM was the only team that actually embarked on the project and didn't only tried to sell a tool/service.,The download of files is terrible Managing the files is terrible,7,We have been more assertive in the places we can open stores, which has a direct impact on the ROI. Measuring the performance of stores and takeing actions on it Targeting behavior of clients,Not yet; it will at some point be connected directly to our data lake.,The cloud computing capabilities of the DSX, the open source would be processed in locus. This would be time consuming.I have to run lot of scenarios in a short time to be able to produce a good estimation. Without the cloud computing it would be impossible.,SAP Leonardo, Oracle R, google tensorflow and Axiom Sales Force Development,Alteryx Analytics, Tableau Desktopdsx_reviewIBM Data Science Experience (DSx) is used in a small R&D group in TI. It is used together with the IBM DB2 product. We use DSx to analyze sensor network related problems such as data correlation and data prediction.,Easy project creation. Large amount of communication resource & example.,Support of python 3 will be nice. Notebook crashes quite often.,7,It extends the our gateway connectivity to cloud and adds analysis capability.,[We chose DSx over an open source tool] Because it allows easier data loading from IBM DB2.,azure and aws,DB2, IBM Watson AnalyticsGreat resource for learningWe primarily use it for internal training and upskilling in IBM products/services and Data Science skills. This experience in turn helps us to better market Watson Studio, and our related services, to our customers and prospects. Finally we also help customers implement, administer/manage, and build solutions using Watson Studio and related components e.g. Jupyter notebooks, Db2, Watson Analytics, etc.,Data ingestion, using Data Refinery and Cloud Object Storage Data persistence, using Db2 Warehouse on Cloud Data manipulation, using Jupyter notebooks, via R or Python scripts Data visualisation, using Pixie dust or Watson Analytics,No ability to audit/log activity Difficult to secure No access to underlying infrastructure Poor reliability and performance Machine learning capability too basic/simple and black box nature means it is difficult to validate/trust Limited ability to customise Watson Analytics visualisation Poor support response times Poor native support for Softlayer S3 storage,7,It certainly helps us as a consultancy and IBM reseller to increase sales revenues (>$200K last year) It has helped customers evaluate IBM technologies and compare them against alternatives in a cost effective and time efficient way It has helped customers implement completely new types of analytic applications and deployments that were not possible for them to perform previously,When we last used DSX with a live customer engagement Data Catalog and Refinery weren't yet available, so we had to use Data Connect instead. What we found was it was extremely limited in terms of capability, and relatively slow in terms of performance and developer productivity. Data Refinery in contrast provides more transformations, better visualisation, improved governance and logging, but we have not used it yet in anger,Watson Studio comes with Jupyter Notebooks and RStudio built in! If it did not, we would not be using it. It is the management and integration of these open source tools (including Spark) with other value-added IBM products like Db2 Warehouse on Cloud, and Watson Analytics that is what makes Watson Studio a more interesting proposition than the open source components alone. In terms of benefit realisation, I think we were definitely able to learn new skills using the environment, but as stated previously, found some of the integrated components e.g. Data Connect and Watson Analytics to be too limited and simplistic for a sophisticated Enterprise customer deployment,AWS,IBM PureData, DataRobot, ThoughtSpotDSx, more for experimenting than experience.DSX is being used to explore and mature ideas but has not yet been broadly adopted. It is being used to manage the analytics process of new ideas and theories as well as provide a common platform for the team to leverage and align on.,Allows for people with various technology backgrounds to use a common platform. Easy implementation due to the cloud availability. The DSX platform allows for junior and citizen data scientists to perform complex actions without needing to have deep knowledge of some of the underlying configuration and setup that generally come with standalone/local analytics tools.,Interfacing with non-IBM technologies is often cumbersome and sometimes restrictive. The interface has undergone a number facelifts which often causes some lost productivity when you need to "find" where things have moved to.,8,At this time the product is still being evaluated so ROI is not yet fully determined.,As my focus has shifted to data governance, the value of the integration with data catalog is a definite benefit that is undervalued. Data factory has continued to improve after each iteration and if this continues, an ability for data factory/catalog capabilities to provide the data lineage for any DSX project would prove very beneficial in supporting regulatory or contractual requirements for data terms of use.,Currently we are still evaluating.,RapidMiner Studio, RStudio and SAS Advanced Analytics,IBM SPSS Modeler, IBM Cognos, Tableau Desktop, RStudioIBM DSxIBM Data Science Experience was used by my organization for projects which required the use of Machine learning. Mainly the R Jupyter notebook was used as well as the SPSS Modeler. It was used by just a department. The main business problem was predicting Pavement condition index for highways.,DSx provides an excellent support for machine learning modeling DSx provides a good environment for collaboration between colleagues DSx also provides support for sharing datasets, models, notebooks, and articles to start projects,They should involve the drag and drop functionality more into DSx for data analysts who are not so much into coding Also, scripting nodes should be integrated into the drag and drop(SPSS MODELER) Also, more nodes should be added to the SPSS Modeler. For example, remove duplicates node, edit metadata node etc.,7,IBM DSx helped us meet some needs of our business clients Not all business users could fully utilize DSx as they don't have much experience in coding Most Business requirements for a problem were solved,No. I mostly work on DSx using data on my local machine. I mostly do all the data cleaning and preparation in other software before loading them into DSx. However, my colleagues' experience with Data catalog and Data Refinery has been a good one. It really reduces time spent getting your data ready for your analysis.,DSx provides you with a very good collaborative environment. Also, the SPSS Modeling tool provides users a quick way (Data Audit node) to identify any correlations between the variables in the dataset. The DSx machine learning can also be deployed in Watson Machine learning environment for consumption. Security in DSx is better as access to your assets is role-based,Microsoft Azure Machine Learning Workbench, Microsoft BI and RStudio,Microsoft Power BI, Microsoft Azure Machine Learning WorkbenchDSX from a BP perspectiveWe are an IBM Business Partner, we sell SPSS solutions and we were involved by IBM to try DSx and see how this new solution can fit our customer's needs. We are seeing DSx as a compliment to SPSS in organizations with users that are more prone to code analytics in open source languages instead of using more traditional tools like Statistics or Modeler.,Collaborative Work Workspace Scalability,SPSS Integration Modeler Canvas development Open source compatibility,8,So far, a few of our customers see or have an application with the tool. Most of our customers are still developing analytics skills, and this tool doesn't help them in their learning curve. On the positive side, this is a step forward to provide new customers with a suite of tools that they understand.,We haven't tested this features yet. As an SPSS specialized firm, we already saw great advantages on this matter when using SPSS solutions (especially Modeler) so it´ll be interesting to see if DSX offers a better way to reduce the time used on data preparation which is a great appeal for most customers.,We are looking forward to seeing how DSX integrates with SPSS, for us and our business it is very important to look for new tools that could help our customers to achieve their analytical objectives. R Studio and Jupyter Notebooks offer the flexibility of use, but we think that having a larger suite of analytical products from IBM that could take advantage of a DSX integration could offer a lead over these solutions; also having the technical support from IBM or one of their BP is something that, for now, we see a differentiator over open source solutions.,IBM SPSS, IBM SPSS Modeler, Anaconda and RStudio,IBM SPSS, IBM SPSS ModelerSuitability of DSx for a Data Science ProjectWe used the DSx platform in the context of a data science project in the medical domain. The general problem was to predict the health condition of a patient in real-time for the upcoming minutes based on various features that were provided by the customer. The status of a patient could be described by a limited number of classes which allowed us to interpret it as a classification problem.Due to confidentiality reasons, we had to perform all tasks on the DSx. This included the analysis of the data set, the computation of additional features, the development and optimization of a machine learning model, as well as the analysis of the results. Therefore, we relied in particular on Jypiter notebooks (python and R) and RStudio,Standard software packages (python and R) are available and ready to run. Data from various sources (e.g. external databases) accessed and loaded from DSx. Customer support provides valuable guidance and helps to solve problems.,An actual IDE for python would be very helpful. Some python packages were not up-to-date and it was not possible to install the current version. It should be somehow possible to monitor the used resources and system load (CPU/RAM).,7,Fewer maintenance costs. The system can be scaled up if necessary. Platform availability is very good.,We only worked with a limited number of data sources. The data sources were always available and we had no latency issues.,In fact, we mostly used Jupyter Notebooks (R and python) as well as RStudio as we were already familiar with those tools.,,RStudio, PyCharm
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IBM Watson Studio (formerly IBM Data Science Experience)
160 Ratings
Score 8.1 out of 101
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IBM Watson Studio Reviews

IBM Watson Studio
160 Ratings
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Score 8.1 out of 101

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February 25, 2019

IBM Watson Studio: Ideal for Rapid Data Science and ML POCs and Deployments with Watson

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Watson Studio is the first third of IBM's new Watson machine learning data pipeline. It's a powerful, reasonably intuitive, low-code environment for building machine learning models and integrating IBM's machine learning APIs (speech recognition, image recognition, etc.) into your ML pipeline. If you already consume Watson APIs, Watson Studio will help streamline current and future deployments.
  • Integration of IBM Watson APIs such as speech to text, image recognition, personality insights, etc.
  • SPSS modeler and neural network model provide no-code environments for data scientists to build pipelines quickly.
  • Enforced best-practices set up POCs for deployment in production with a minimum of re-work.
  • Estimator validation lets data scientists test and prove different models.
  • Watson Studio's UI is not always intuitive, especially when it comes to requirements and specific settings.
  • Documentation is not strong; tutorials and walkthroughs are noticeably light.
  • Tight integration with IBM APIs also means less well-made integrations to third party data sources and APIs—MySQL support notably absent.
Watson Studio is optimal for experienced data scientists and machine learning professionals to develop and deploy models quickly while enforcing best practices that set up projects for deployment and management down the road. It's not appropriate for people without a data science or machine learning background for production use; the ease of the visual modelers does not mean it makes machine learning easy or intuitive.
Read Christopher Penn's full review
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February 15, 2019

IBM Watson studios , our first steps

Score 8 out of 10
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It is currently used by the data analytics department. It is used to address cost analysis and actuarial analysis.-

  • It is relatively easy to use
  • It works seamlessly with multiple languages.
  • Its administration is surprisingly easy
  • And it's easy to install / upgrade / maintain
  • Need better training materials for data scientists. Especially the ones who are not formally educated as data scientists.
  • The videos in the tutorials are all on Youtube which are usually blocked on most work campuses.
  • And the IBM Think campus training could have been better as well
Well suited for my organization's claim diagnosis level analysis across the years.

Less suited for lower level data analysis which does not add much value.
Read ashish devassy's full review
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February 14, 2019

Watson Studio review

Score 5 out of 10
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Verified User
Review Source
We currently use Watson Studio for NLP/NLU purposes in our client environments. It addressed problems in physical space interactions between front line associates and customers. We also leverage tonality and sentiment models in addition to transcription to effectively process large amounts of voice interactions in the physical world. Watson Studio has proven useful in addressing these problems in these environments, but we are limited in our capacity to roll it out further due to edge compute limitations with the platform.
  • Speech to text
  • Keyword analysis
  • Tonality
  • Sentiment
  • Architectural support team to get up and running
Well suited for cloud-based environments, and less suited for edge-based processing.
Read Van West's full review
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February 15, 2019

Great first impression and lot of opportunities with Watson studio

Score 10 out of 10
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Verified User
Review Source
We have rules based applications that help airlines make real time decisions. We are to embed Watson studio to use historical data and optimize the outcomes of those decisions.
  • Intuitive GUI for us to begin using the studio
  • It works well that we can embed the decisions into our existing offering without a lot of changes
  • The pricing model is flexible
  • Like the opportunity to embed more data
  • Provide hint to use services based on verticals
  • Suggest how it could be embedded into mobile apps
  • Would like to understand the deployment model better
Well suited for:
1. Offering ancillary upgrades to airline passengers

2. Predicting flight delays based on historical patterns coupled with live feeds like weather
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February 14, 2019

Watson Studio makes product development easy

Score 8 out of 10
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We use it to create products to detect diseases from skin images so that the doctors can monitor the progress of the diseases and deliver the best therapy. It is being used by the whole organisation as we are a medical AI company and the use of AI is integral to our product.
  • It is easy to use and I don't need to have a team full of data scientists to use them
  • It is easy to deploy when the models are trained and we don't need to hire many software engineers to take care of deployment
  • It allows us to test different models rapidly and so helps to accelerate the product development process
  • The cost is steep and so only companies with resources can afford it
  • It will be nice to have Chinese versions so that Chinese engineers can also use it easily
  • It takes a while to learn how to input different kinds of skin defects for detection
If a company is well resourced, I would recommend it as it allows the software developers to build products quickly and test them, on top of the many features available for advanced users to try. If a company is less well resourced, then it may be more challenging and in this case, the total TCO needs to be analyzed.
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February 22, 2019

Analytics for the Masses

Score 10 out of 10
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Verified User
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We are working to leverage data analytics using an on-premises deployment to aid in predicting faults for our customers in a proactive/reactive manner. We are looking to leverage efficient and regularly trained algorithms in our Diagnostics Engine/BPMS to reduce our overall time to handle and potentially eliminate tickets opened by our customers
  • Ease of use and quick to explore
  • Guided experiences and ability to leverage multiple algorithms to identify the best one
  • Great support and sales teams
  • There isn’t much I think I can provide critical or improvement feedback on
The guided nature of the front end of DSX/WS truly enable an “easy-button” for casual business analysts/scientist, and the advanced functionality using SPSS is a fantastic blend if easy/advanced AI/Statistics
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February 14, 2019

Watson Studio - Data Science Platform

Score 9 out of 10
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Verified User
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Watson Studio is a wonderful product. Currently, I am a SPSS Modeler user and we are looking at migrating to Watson Studio Local. Watson Studio offers a great development and deployment platform for data scientist.
  • Flexibility in the use of different data science development environment, e.g. R, Python and SPSS modeler
  • Deployment capability in miroservices
  • Nothing at the moment
It offers an end to end solution for data science analytics
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April 06, 2018

The Most Robust Available

Score 8 out of 10
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Verified User
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My organization is currently going thru an expansion process, from being solely a consulting company to creating and developing products itself and for this expansion, a new team was assembled, the Research & Development team and I'm part of this team. As part of our research to create products that are really relevant to the market we are aiming at, and since we're already are a gold level IBM partner organization, it made sense for us to acquire knowledge with IBM DSx in order to maximize our efficiency and develop better products.
  • Feature rich. IBM DSx provides a plethora of tools to leverage the use of data science in your organization and suit your specific needs.
  • IBM DSx supports a huge variety of sources of data. From your traditional SQL database to every major data warehouse, IBM DSx does a great job at connecting to or pulling from your data source.
  • Its greatest strength is the fact that is a cloud-based service. There's no need to waste time on configuring and maintaining an environment to start analyzing data, which may not be an easy task.
  • Pricing. The price for this product is quite steep and, since it features so many solutions, it makes sense to cost as much as it does. But the creation of personal plans with fewer features might prove interesting to bring the product to a broader audience, like enthusiasts that are starting to get in touch with data science.
  • Some issues regarding notebooks and the use of data refinery are quite annoying to the experience because, depending on the use that you make of it, they might appear quite regularly.
  • Lack of a changelog. Like many IBM products and platforms, DSx is in constant development and is updated regularly. This is a great point, except for the fact that sometimes it lacks a changelog to properly inform what has been changed, requiring the user to investigate on its own.
I believe IBM DSx is a great fit for organizations that are engaged deeply in data science and are looking for a solution that is able to both leverage the efficiency of their actual work and train additional data scientists since it also features many tutorials to increase the knowledge for its users. I don't think it is the appropriate product for a full group of starters on data science and/or organizations that plan on using data science on a small scale because of its price and the high number of features.
Read Pedro Henrique de Almeida's full review
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May 08, 2018

Watson vs. DATA

Score 10 out of 10
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Verified User
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Watson Studio is used within Manifest Life Inc to help gather, process, and visualize various amounts of data. We use Watson Studio throughout our data management lifecycle and view it as the best all-in-one data management offering within the market. Watson Studio has helped our organization manage our data operations more fluidly through the offerings ease of use and minimal learning barrier. This product from IBM works like a charm and is flexible in small and/or large business environments. Data sets of all size can be easily cleaned, processed and leveraged in order to help meet our business objectives and create new opportunities.
  • Cloud-based file sharing helped our organization stay up to date when managing assets, new or old.
  • Watson studio does a fantastic job visualizing outcome data which enabled our organization to easily create a narrative based on what we were able to see.
  • Particularly within our organization, Watson Studio strength was noticed in its ability to processes enormous amounts of data in such a short amount of time.
  • Watson Studio could used improvement in its user-based community. I'd like to see more local and remote events showcasing its potential.
  • Watson Studio could improve by providing its users, use-cases that leverage data in unusual ways.
  • We think Watson studio could also improve by decreasing its price in order to capture new talent in the data industry.
We would highly recommend Watson Studio to any of our colleagues interested in combining machine learning and data management due to its wide range of capabilities and ease of use.
Read Isaiah King's full review
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April 17, 2018

I used to swear by IBM DSX

Score 1 out of 10
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Verified User
Review Source
I used to teach with Watson Studio, but not anymore. It is inconsistent in many ways it breaks and when I ask for help, no one seems to be bothered.

When I do get a response, it doesn't solve my problems. IBM needs to get it's act together or risk losing out.
  • Good UI
  • Easy of use
  • Incorporates both Python and R
  • Could be more holistic
  • Have a look at Microsoft Studio, it's well integrated
  • Not very well thought out
Abundant use cases in the community section are very helpful.
Using R and Python interfaces on the same platform has great flexibility.
Drop down menus are great! Nothing needs changing there!
Apache Spark connection seems to be unstable.
The H2o package in R is erratic.
Apache spark in R could use some documentation - solid documentation.
Read Dr. George Ng's full review
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April 05, 2018

IBM Data Science Experience (DSx) for big data analysis

Score 8 out of 10
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Verified User
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We use IBM data science experience in order to create and train predictive analytics model: we implement some use cases about this topic, using different models. We've focused on model referring to vehicle registration using historical models and predictive variables like oil trend. Also, we use Jupyter Notebooks to analyze Twitter data and create data visualizations.
  • You can use SPSS model in order to predict trend with historical data
  • You can use R in order to clean up your data a Jupyter notebook
  • You can use Jupyter Notebooks to analyze Twitter data and create data visualizations
  • We try to install DSX in the local environment but it needs more resources
  • I'd like a better visualization library for charts
  • I'd like more webinar in order to introduce to the platform, also in Italian language
I appreciate IBM data science experience for creating an spss model and for working with R language. My project created with SPSS work in DSX ;: the import procedure is very good. With respect to SPSS, there is less model. The procedure for enabling my account is not simple and I've had only 30 days to try the platform.
Read Andrea Bardone's full review
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March 30, 2018

Beginner developer for DSx

Score 9 out of 10
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It was being used to analyze big data. It was being used in our department for development purposes, and testing its limits and capabilities. The software was still in development when I was working on it, and my job was to create intro level content displaying the different types of services the platform offered.
  • Exploratory data analysis
  • Concurrent project and data management
  • Built-in libraries and frameworks for different programming languages
  • The data limit was low, but it depends on how much you pay
Any data analysis in Python or R is very simple to do with DSx, as the notebooks allow for easy organization of data and testing code. Using notebooks allows code to be run in sections allowing for bug-testing, but also step by step analysis which can be visualized to determine the impact and meaning of the data.
Read Dimitrije Glumac's full review
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March 26, 2018

IBM DSx, soon to be even more productive as Watson Studio

Score 8 out of 10
Vetted Review
Verified User
Review Source
I use DSx both for internal projects as well as for clients. Depending on the situation I'll sometimes use DSx desktop and the cloud service other times. Most of my applications represent some form of analytical pipeline and are used for experimentation and/or development rather than any kind of production use.
  • DSx is particularly well suited for ML data prep. It's easy to ingest from many different kinds of sources and then perform various cleansing, transformation, and enrichment operations.
  • DSx makes collaboration with other team members very easy. Control over who can see and interact with each project is straightforward and simple to administer.
  • DSx doesn't create proprietary lock-in. Notebooks can be exported in a number of different forms to share people that don't use DSx or to run in a different environment.
  • Stability has gradually improved over the past year or so, but could still be better.
  • I'd like to see options for leveraging a GPU on the cloud-hosted version.
  • I'd like to see even more ML model lifecycle support, but it's my understanding that this is coming with the move to Watson Studio.
DSx is a wonderful tool for certain kinds of applications such as experimenting or prototyping. It's probably not a great fit for production applications.
Read Jim Sharpe's full review
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March 20, 2018

Pretty good, but still away from perfect

Score 9 out of 10
Vetted Review
Verified User
Review Source
We have 10 users. It is used by the analytics team and by some other citizen data scientists capable of making use of it. We mainly use it for problems big enough that we are not able to tackle in our on-premise servers.
  • Integration with Spark
  • Jupyter-like environment
  • Asset and community access
  • Easier access to data
  • Connection with on-premise datasources
  • Personalization
It is well suited for a highly skilled team of data scientists, with needs of processing large quantities of data.
Read Sergio Pulido Tamayo's full review
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March 19, 2018

My thoughts on DSX

Score 9 out of 10
Vetted Review
Verified User
Review Source
We were using DSx for cleansing and modeling data for the insurance industry. DAD has been used for our small data science team only.
  • To have Jupyter notebooks and RStudio in the same environment is great!
  • The free Spark engine is perfect and enough to support the development activities.
  • The integration with GitHub facilitated our collaborative work.
  • To set up a new Spark cluster and use it with DSx is a bit hard. It would be great to have the option to create a new big cluster without leaving DSx.
  • I've faced several problems with DSx desktop.
IBM Data Science Experience Is a very flexible and handy environment; ideal to develop and debug your data science code.
Read Jose Valdivia Leon's full review
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March 06, 2018

Forget the configuration. Use DSx.

Score 9 out of 10
Vetted Review
Verified User
Review Source
We use DSX in the development department. Previously, we spent a lot of time configuring our machines, setting up the computers and the virtual environments, getting the libraries. We can show our work to our boss without going with our computers. WIth DSX, we are able to do our work in the platform and share it with whoever we want. Then, they can leave comments in the notebook and we can review them later.
  • Configuration: You can forget about all the setup. You just open a notebook, import the libraries you want and start writing
  • Sharing: You can share your thoughts with anyone, because all your code lives in the cloud
  • Tools: IBM has amazing tools for speech recognition, image processing, and so on
  • Because of my use, I didn't find anything to improve. I think that making things more visual will be useful for non-expert people, like flowcharts for example
I felt quite comfortable using DSX for prototyping. I was able to build an interesting model in less than two weeks, and I found it to be very productive to be able to share my ideas with the client and receive back their comments.

After that, I had to implement this model to be used as a REST service. I tried to do that with DSX but it was not possible, which is reasonable since it is not designed to do that.
Read Facundo Ferrín's full review
Sebastian Ferro profile photo
April 13, 2018

Watson Studio opinion

Score 6 out of 10
Vetted Review
Verified User
Review Source
As an IBM partner, my company is evaluating this product for potential use for our clients. Usually we offer data mining projects in order to meet specifics needs our clients demand. As a example we develop models for risk prediction across many financial organizations.
  • I like very much to have a multitude of tools integrated nicely in one place.
  • The documentation is ever-growing and a source of continuous new insights. I appreciate the notable curatorial effort.
  • The tools and services are ever-growing too. The focus on open-source tools and the "no barriers in the middle" is really one of the strongest points in my opinion.
  • Well, I had issues of performance and responsivity.
  • As a tool for exploring new ideas and learning new techniques the free plan is limited to very small datasets in order to perform in a reasonable time.
  • As an example a simple test of Random Forest on a dataset of 100k rows didn't finish in the RStudio provided by Watson whereas it took half an hour in the RStudio installed in my desktop.
I think it's well suited for a large organization but no so for a small one.
Read Sebastian Ferro's full review
Sandra Sarahí Ruvalcaba Chávez profile photo
March 27, 2018

DSx Review

Score 10 out of 10
Vetted Review
Verified User
Review Source
It is used to create demos of the tool and real cases for selling it. In my organization it is not used.
  • The run period is good and what you run is saved.
  • You have to install nothing and that's a great advantage.
  • You can create conections to save your files in the cloud.
  • It works perfectly in a collaborative environment and it's easy.
  • It is limited still. Jupyter supports many programming languages but DSx just manages three.
  • The scheduled jobs are fantastic but it can be more frequent.
  • The multiple errors of the account, the group, the services are confusing.
DSX is perfect when you want to work in the cloud. DSx is not recommended when you work with your own files on your PC and you want to upload your own files on your PC.
Read Sandra Sarahí Ruvalcaba Chávez's full review
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March 15, 2018

Market Simulator with DSX

Score 7 out of 10
Vetted Review
Verified User
Review Source
DSX is been used by my department only as a pilot project to prove the tool capabilities, although we are currently doing different projects: one to simulate the Brazilian retail market to find the best places to open stores, measure performance of existing stores, another to read verbatim text from our clients, and a third to correlate sales behavior with population cluster.
  • Flexibility: all the other solutions we procure were modular with black boxes.
  • Data center is in Europe, since in my company we can't have data in US
  • From all the solutions we procure IBM was the only team that actually embarked on the project and didn't only tried to sell a tool/service.
  • The download of files is terrible
  • Managing the files is terrible
This product is good for specific needs of creation, since you have to build the solutions from scratch.
Read Marcus Vinicius Velleca Bernardi's full review
Jianwei Zhou profile photo
March 01, 2018

dsx_review

Score 7 out of 10
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IBM Data Science Experience (DSx) is used in a small R&D group in TI. It is used together with the IBM DB2 product. We use DSx to analyze sensor network related problems such as data correlation and data prediction.
  • Easy project creation.
  • Large amount of communication resource & example.
  • Support of python 3 will be nice.
  • Notebook crashes quite often.
General user experience is good, but the data loading from IBM DB2 is very slow initially.
Read Jianwei Zhou's full review
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April 16, 2018

Great resource for learning

Score 7 out of 10
Vetted Review
Reseller
Review Source
We primarily use it for internal training and upskilling in IBM products/services and Data Science skills. This experience in turn helps us to better market Watson Studio, and our related services, to our customers and prospects. Finally we also help customers implement, administer/manage, and build solutions using Watson Studio and related components e.g. Jupyter notebooks, Db2, Watson Analytics, etc.
  • Data ingestion, using Data Refinery and Cloud Object Storage
  • Data persistence, using Db2 Warehouse on Cloud
  • Data manipulation, using Jupyter notebooks, via R or Python scripts
  • Data visualisation, using Pixie dust or Watson Analytics
  • No ability to audit/log activity
  • Difficult to secure
  • No access to underlying infrastructure
  • Poor reliability and performance
  • Machine learning capability too basic/simple and black box nature means it is difficult to validate/trust
  • Limited ability to customise Watson Analytics visualisation
  • Poor support response times
  • Poor native support for Softlayer S3 storage
It's a fantastic environment for learning basic Data Science skills; working with insensitive, non-production data; and performing simple data visualisations. It is completely unsuited to enterprise production deployments requiring advanced auditing and security; sophisticated and transparent machine learning; complex analytics and custom data visualisations.
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April 06, 2018

DSx, more for experimenting than experience.

Score 8 out of 10
Vetted Review
Verified User
Review Source
DSX is being used to explore and mature ideas but has not yet been broadly adopted. It is being used to manage the analytics process of new ideas and theories as well as provide a common platform for the team to leverage and align on.
  • Allows for people with various technology backgrounds to use a common platform.
  • Easy implementation due to the cloud availability.
  • The DSX platform allows for junior and citizen data scientists to perform complex actions without needing to have deep knowledge of some of the underlying configuration and setup that generally come with standalone/local analytics tools.
  • Interfacing with non-IBM technologies is often cumbersome and sometimes restrictive.
  • The interface has undergone a number facelifts which often causes some lost productivity when you need to "find" where things have moved to.
Data science ideation and POC is definitely a sweet spot in my opinion of DSX. It is easy to get up and running and can elevate people that have the business knowledge but lack some of the senior science skills to be proficient analytics users.

Moving the models developed to a production ready model is not an easy path and often taking the analytics idea to a product involves translating the method and approach to other tools.
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March 21, 2018

IBM DSx

Score 7 out of 10
Vetted Review
Verified User
Review Source
IBM Data Science Experience was used by my organization for projects which required the use of Machine learning. Mainly the R Jupyter notebook was used as well as the SPSS Modeler. It was used by just a department. The main business problem was predicting Pavement condition index for highways.
  • DSx provides an excellent support for machine learning modeling
  • DSx provides a good environment for collaboration between colleagues
  • DSx also provides support for sharing datasets, models, notebooks, and articles to start projects
  • They should involve the drag and drop functionality more into DSx for data analysts who are not so much into coding
  • Also, scripting nodes should be integrated into the drag and drop(SPSS MODELER)
  • Also, more nodes should be added to the SPSS Modeler. For example, remove duplicates node, edit metadata node etc.
IBM DSx is best suited for creating cloud-based machine learning modeling. Its support for open-source software such as Python and R is a plus. For Data Analyst who prefers writing codes for all their algorithms, DSx is a better place to do this. The latest packages for the software can be added and installed easily too.
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March 21, 2018

DSX from a BP perspective

Score 8 out of 10
Vetted Review
Reseller
Review Source
We are an IBM Business Partner, we sell SPSS solutions and we were involved by IBM to try DSx and see how this new solution can fit our customer's needs. We are seeing DSx as a compliment to SPSS in organizations with users that are more prone to code analytics in open source languages instead of using more traditional tools like Statistics or Modeler.
  • Collaborative Work
  • Workspace
  • Scalability
  • SPSS Integration
  • Modeler Canvas development
  • Open source compatibility
For organizations that already have a data science department in place. Not for business users that need to have fast access to results in order to meet business demands.

DSX is fulfilling the capabilities that all users need. We see the main core of users on people who want to experiment with analytics, algorithms, and techniques before going full business implementation.
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March 14, 2018

Suitability of DSx for a Data Science Project

Score 7 out of 10
Vetted Review
Verified User
Review Source

We used the DSx platform in the context of a data science project in the medical domain. The general problem was to predict the health condition of a patient in real-time for the upcoming minutes based on various features that were provided by the customer. The status of a patient could be described by a limited number of classes which allowed us to interpret it as a classification problem.

Due to confidentiality reasons, we had to perform all tasks on the DSx. This included the analysis of the data set, the computation of additional features, the development and optimization of a machine learning model, as well as the analysis of the results. Therefore, we relied in particular on Jypiter notebooks (python and R) and RStudio


  • Standard software packages (python and R) are available and ready to run.
  • Data from various sources (e.g. external databases) accessed and loaded from DSx.
  • Customer support provides valuable guidance and helps to solve problems.
  • An actual IDE for python would be very helpful.
  • Some python packages were not up-to-date and it was not possible to install the current version.
  • It should be somehow possible to monitor the used resources and system load (CPU/RAM).
The DSx platform is an appropriate choice if a project cannot be carried out on on-premise systems. It provides standard data science software packages that are directly ready-to-use. However, the DSx imposes also some limitations that could be an issue for some projects. For instance, it might not be possible to install required non-standard software.
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Feature Scorecard Summary

Connect to Multiple Data Sources (7)
8.5
Extend Existing Data Sources (7)
8.0
Automatic Data Format Detection (7)
7.0
MDM Integration (3)
6.2
Visualization (7)
8.0
Interactive Data Analysis (7)
8.0
Interactive Data Cleaning and Enrichment (7)
8.3
Data Transformations (7)
8.0
Data Encryption (6)
8.4
Built-in Processors (7)
8.7
Multiple Model Development Languages and Tools (6)
9.4
Automated Machine Learning (7)
9.0
Single platform for multiple model development (7)
8.7
Self-Service Model Delivery (7)
9.2
Flexible Model Publishing Options (7)
8.3
Security, Governance, and Cost Controls (7)
8.5

About IBM Watson Studio

IBM Watson Studio (formerly IBM Data Science Experience) is a collaborative, cloud-based environment providing data scientists with a variety of tools including RStudio, Jupyter, Python, Scala, Spark, IBM Watson Machine Learning, and more.
Categories:  Data Science

IBM Watson Studio Technical Details

Operating Systems: Unspecified
Mobile Application:No