IBM Watson Studio Reviews

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November 04, 2019

Watson Studio Review

Score 6 out of 10
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It is used in the form of value chain at a macro level to drive optimization value and manage unplanned upsets. It is used by my department.
  • It is very user friendly.
  • Secure and can have federation security.
  • Very quick and high-resolution visual graphics.
  • Advanced modeling techniques.
  • It should have the capability to utilize thermodynamic models and extract key values.
Watson Studio is great for visualization and model computing, however, it doesn’t have error handling capability inbuilt.
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Christopher Penn profile photo
Score 10 out of 10
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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.
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ashish devassy profile photo
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.
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Van West profile photo
February 14, 2019

Watson Studio review

Score 5 out of 10
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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.
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Score 10 out of 10
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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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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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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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Score 9 out of 10
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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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Pedro Henrique de Almeida profile photo
Score 8 out of 10
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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.
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Bhaumik Pandya profile photo
February 23, 2018

DSx - as a beginner

Score 8 out of 10
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DSX is being used by a sub-department of our company.

We are using it for the projects of service-automation and recommendation systems to analyse data and build models.
  • Can connect to IBM DB2 - Data Warehouse and has integrated IDEs for Data-Scientists including RStudio, Jupyter Notebooks and SQL-Dashboard.
  • A version of DSX, DSX-Desktop, makes it quite easy to play with your data and is powered by Spark.
  • Access to ML Libs such as, Python Sci-kit Learn makes it simple to not only apply the model over data and optimize it, but also to deploy to Watson Machine Learning service for production purposes.
  • I would love to deploy the R-models for production.
Consider an ecosystem or application backend, that has different databases with different types of data (structured, documents etc.). Normally, a simple analytics for such data sources requires to fetch and query data from multiple sources and build visualizations for them. With DSX RStudio or Jupyter and DashDB (or DB2), all data can be accessed at one point. This makes it, as per the definition, a more practical approach than Data Lakes, where you can also build and deploy ML models. Almost everything that a data scientist needs.
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José Adolfo Ramírez Magdaleno profile photo
Score 9 out of 10
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To move data from to an API public government service to an operational dashboard that shows real time results.

All the connections and data preparation jobs are achieved with DSX through a Python Jupyter notebook which runs automatically every 10 minutes and solves the whole process without human intervention.
  • Scalable in the sense that its performance can grow without complications, but also in its capabilities, since various services can be included at a very competitive price: optimization, machine learning, storage, etc.
  • Collaborative solution, since you do not work in isolation, you can generate data science projects with your peers, manage permissions, manage versions of the script.
  • Enabled in Spark, the top framework for data science and machine learning.
  • It would be very valuable to include a calculator that will help you identify how many cores or require hiring Spark resources and storage resources, to make a precise sizing from the start.
Ideal for companies that have teams (3 to more professionals) of data scientists who need to guarantee results at all times and without breaks, in the sense that DSX is in a cloud that does not require installations or dependencies of the IT department.
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Colin Sumter profile photo
Score 8 out of 10
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We bid on specialized projects in the public sector using a confidential configuration that solves a variety of problems. Marketing Research, Unified Data Governance, Modern Call Center, and other Cloud Initiatives. It's an integral part of our training for Engagement, Conversion, and Fulfillment of our BPM. This plans optimization segments by use case rather than dedicating resources to a single Industry like everyone else.
  • Rapid Data Science; IBM has done a great job of automating the data prep.
  • Asset Classification; there's a way to use numerical language prediction vs. natural language processing by using highly specialized data classification catalog within the DSx.
  • Visual Data Modeling; this results in faster time to value because you won't spend all day tuning a data model. Allowing you to compare different data models in the same weather forecast.
  • There should be a heavier emphasis on the IBM DSx Community. Showing people where to begin.
  • Perhaps more information related to how data science improves an organizations competency inventory to reduce the intimidation factors.
It's suited for big data analytics. Some industries would Pharma data, Oil & Gas, Financial Markets, HR, Sales, Price Variance, and etc. Most professionals do repetitive data prep on static spreadsheets. DSx reduces the data duplication and allows everyone to see the same picture. And makes segregation of duties simple: the modeler can be isolated from the data tuning operation and both can be isolated from the decision maker.
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Isaiah King profile photo
May 08, 2018

Watson vs. DATA

Score 10 out of 10
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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.
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Dr. George Ng profile photo
Score 1 out of 10
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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.
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Andrea Bardone profile photo
Score 8 out of 10
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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.
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Dimitrije Glumac profile photo
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.
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Jim Sharpe profile photo
Score 8 out of 10
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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.
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Sergio Pulido Tamayo profile photo
Score 9 out of 10
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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.
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Jose Valdivia Leon profile photo
March 19, 2018

My thoughts on DSX

Score 9 out of 10
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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.
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Facundo Ferrín profile photo
Score 9 out of 10
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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.
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Ben Holmes profile photo
Score 10 out of 10
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DSx is our area's advanced analytics platform. The product is new and so only our team uses DSx. Specifically, we have built a simulation program that will build financial projections for upcoming changes in our products. The health insurance industry has been in flux for several years, and there is greater value than in previous times on faster analytic turnaround. DSx provides the speed to spin up business concepts, provide financial impacts to the business concept, and spin down without heavy time investment. Therefore, our area can return results to our business areas with a much more robust set of problems (previously unapproachable due to time and resource costs).
  • Very low administrative cost. Often, obtaining new technology/software can cause undue burdens on IT administration. IBM handles all of that from the cloud-based server, and so I just get to work. Our IT area is not needed to maintain its infrastructure, software releases, etc.
  • Mixes the best of proprietary and open-source benefits. Though all the open-source modules are available for integration into DSx, IBM provisions a large library packages and even sample code that are maintained by IBM. This allows me to have the good "spoon-fed" options for building analytics provided by IBM directly, or to engage github/stackoverflow for any code, modules I might need for a particular situation.
  • Lots of user interfaces for difficult coding situations. DSx has a SPSS model builder, and that's a tremendous help in building predictive models without having to know code. Additionally, there are a wide variety of tools for various analytical problems (Data Refinery, Data Catalog, Data Governance) which provider interfaces, rather than code intensive. A user wouldn't need to be a programmer to use, probably just some background in SQL would be sufficient.
  • The stability of the application itself, though it has improved greatly, still struggles at times. I'd say about once a month I run into an issue where something is very slow or keeps crashing, typically lasting only a few hours.
  • At the moment, pretty much stuck with a Jupyter notebook (unless using RStudio which I don't know much R yet), and I would like to use some of the improved Jupyter environments with enhanced user interfacing (Jupyter Plus). Not available at this time.
  • Needs an interface for the server file directory (like windows explorer). Sorta pain to write in a scala notebook from java to access the local present working directory. Python is clearly easier with the "magics." Still I think a file interface would be nice for the server itself, maybe even for established dataframes in-memory.
DSx is great for fast turnaround analytics, even mini-research studies on business issues. The open-source modules like Pandas and Spark are much more efficient than the standard proprietary analytics platforms (Our company uses SAS a lot). In situations where a fast answer is needed, DSx is great. Also, in building and testing out predictive models, DSx is great. For production level integration, where a set of code needs deployed into production, DSx isn't made for that. Best to use a programming specific platform for those instances.
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Mauricio Quiroga-Pascal Ortega profile photo
Score 10 out of 10
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As a senior consultant, I'm always looking to discover value in our's client data. This is crucial in our commercial model. Moreover, we always calculate the NPV of all the opportunities that we detect.I have been using DSx for anomalies detection in time series, understanding bias and variances and developing machine learning algorithms to estimate key dependent variables propensities.

Mainly, we have been helping retailers and their suppliers on stock optimization, price elasticity, and in-store stock shortage estimations.
  • Very easy to use. Very intuitive.
  • It's very easy to export the data architecture of a project and use it and modify it in a new project
  • Having a very powerful cloud processing capability, allow to perform complex data analytics in any place with a good Internet connection
  • At the beginning it's a little complex to understand some of the interface distribution
  • Since the features are changing continuously, some of the tutorials don't fit the current version.
  • Sometimes, the cloud platform run very slowly.
Well suited:
  • Develop a complex solution for a client with very big data
  • Organize working between several data scientists in separate locations
Less Appropriate:
  • When there's a need to make a fast opportunity assessment and the client's data isn't pre-processed
  • When you're working with other data scientisst that are "addicted" to Phyton and R
Read Mauricio Quiroga-Pascal Ortega's full review
Gonzalo Angeleri profile photo
Score 8 out of 10
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My organisation uses IBM Data Science Experience (DSx) as part of a customer experience solution to build machine learning models to calculate several customer metrics as future value and churn.
  • Visual Environment to develop models
  • The ability to use R-Studio on the web
  • The ability to use SPSS models
  • Working with large files take time to upload
  • I would be great to have a place to exchange models and examples with other users
  • Data science users or developers who need to design and build machine learning models.
  • Also, I find DSX a very good tool to help with collaboration between different teams.
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Davide Tognon profile photo
Score 8 out of 10
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We use IBM DSx in the whole company, mostly in the field of research; we address it to predictive and descriptive analysis.
  • DSx is very useful for viewing data from different points of view (via Watson Analytics)
  • DSx is very powerful with its Python and R ambient integrated within
  • DSx is a good tool for collaborative machine learning procedures
  • DSx ...
DSx very good for descriptive and predictive analysis. We gifted our manager with this new tool so that our equipment can be used for research purposes based upon analysis and new features that could be achieved after a predictive analysis. We use both of them, inverting the order, sometimes, testing our ideas or being inspired by a different point of view upon our data.
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Anferny Chen, MBA profile photo
Score 10 out of 10
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Just a small team is trying to use it internally and for establishing a collaborative working relationship with clients on selected projects.
  • The collaboration with team members and clients is a big plus. And it is easy to use.
  • The ability for me to use Python and SPSS Modeler helped me put together a comprehensive analysis in a more timely manner.
  • The loading time for files seems to be a bit long.
It is well suited for collaboration with a team of diversified talents - technical and managerial/non-technical - and with clients. It might be less appropriate if I am working with only advanced machine learning professionals and data scientists (using the open platform like Python would seem a more efficient choice)
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Feature Scorecard Summary

Connect to Multiple Data Sources (8)
Extend Existing Data Sources (8)
Automatic Data Format Detection (8)
MDM Integration (4)
Visualization (8)
Interactive Data Analysis (8)
Interactive Data Cleaning and Enrichment (8)
Data Transformations (7)
Data Encryption (7)
Built-in Processors (8)
Multiple Model Development Languages and Tools (7)
Automated Machine Learning (8)
Single platform for multiple model development (8)
Self-Service Model Delivery (8)
Flexible Model Publishing Options (8)
Security, Governance, and Cost Controls (8)

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

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Mobile Application:No