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The 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 MaximoDSx - as a beginnerDSX 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.,8,Positive impact: Reduces effort significantly.,No. All of the DSX projects have been linked with IBM DB2 Warehouse on Cloud. The databases are on-a-prehand-basis warehoused.,Open source tools are cool, really. In case of DSX, most of the open-source tools or libraries are available on/with DSX. But using open source tools add a huge effort to manage everything on your own and hence add risks too. To avoid this effort and risks, I would use a managed service like DSX.,RStudio and MATLAB,RStudio, Tableau DesktopDSX: a cloud solution to make data science in the company a realityTo 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.,9,Totally ROI guaranteed, since you can even start with a free LITE version and start from there according to the needs.,Of course, 0 installations, 0 configurations, it's just amazing how with DSX you can start working from day 1. Additionally, data preparation services and as scheduling tasks that are available would save you development hours, I recommend taking advantage of DSX's own resources to generate better and faster data science projects.,The solution for data scientists that has it all and can integrate the work of these scientists to the process of the entire company, I think it is one of the best parts, with the services of Watson Data Platform DSX is not isolated from the business process, it is integrated in a very natural way to the data engineers and developer processes.,IBM SPSS Modeler, IBM Watson Analytics, IBM Watson Campaign Automation, QuestionPro, SurveyMonkey, IBM SPSSCooking With Gas - IBM DSx Is Your New Wave Igniter FluidWe 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.,8,We specialize in wireless infrastructure as a service. ROI is in the form of no appliance maintenance, lowered utility costs, scalability, and augmented intelligence. We're able to look at 100+ attributes vs. another person only being able to consume 10 attributes before they get tired.,We're able to quickly model a Pharma machine learning demo (not production data) data manually that took hours. Data Catalog and Data Refinery let us segment and visualize the data before modeling it. This gave us great inference to our (transversal steps) - "ASK". Then go back and understand another public data set to create a cross section using the SPSS in the same weather forecast. Leading to a more comprehensive picture sooner because manually coding the Scala notebook took hours. Establishing our confidential PoC.,We're resellers of the DSX. For our security, we do not open any other cloud accounts.,Watson 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,10
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Watson Studio (formerly IBM Data Science Experience)
91 Ratings
Score 7.4 out of 101
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IBM Watson Studio Reviews

IBM Watson Studio
91 Ratings
Score 7.4 out of 101
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April 06, 2018

IBM Watson Studio Review: "The Most Robust Available"

Score 8 out of 10
Vetted Review
Verified User
Review Source
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
February 23, 2018

IBM Watson Studio Review: "DSx - as a beginner"

Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
Read Bhaumik Pandya's full review
February 22, 2018

IBM Watson Studio Review: "DSX: a cloud solution to make data science in the company a reality"

Score 9 out of 10
Vetted Review
Reseller
Review Source
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.
Read José Adolfo Ramírez Magdaleno's full review
January 23, 2018

IBM Watson Studio Review: "Cooking With Gas - IBM DSx Is Your New Wave Igniter Fluid"

Score 8 out of 10
Vetted Review
Reseller
Review Source
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.
Read Colin Sumter's full review
May 08, 2018

IBM Watson Studio Review: "Watson vs. DATA"

Score 10 out of 10
Vetted Review
Verified User
Review Source
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
April 17, 2018

IBM Watson Studio Review: "I used to swear by IBM DSX"

Score 1 out of 10
Vetted Review
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
April 05, 2018

IBM Watson Studio Review: "IBM Data Science Experience (DSx) for big data analysis"

Score 8 out of 10
Vetted Review
Verified User
Review Source
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
March 30, 2018

IBM Watson Studio Review: "Beginner developer for DSx"

Score 9 out of 10
Vetted Review
Verified User
Review Source
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
March 26, 2018

IBM Watson Studio Review: "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
March 20, 2018

IBM Watson Studio Review: "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
March 19, 2018

IBM Watson Studio Review: "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
March 06, 2018

IBM Watson Studio Review: "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
February 22, 2018

IBM Watson Studio Review: "Great for non-programmer analyst"

Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
Read Ben Holmes's full review
February 22, 2018

IBM Watson Studio Review: "DSx for Consulting Assessment"

Score 10 out of 10
Vetted Review
Reseller
Review Source
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
February 20, 2018

IBM Watson Studio: "DSX Review - Customer Loyalty Models"

Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
Read Gonzalo Angeleri's full review
January 26, 2018

IBM Watson Studio Review: "Welcome to DSx ... DSx is welcomed by us!"

Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
Read Davide Tognon's full review
January 26, 2018

IBM Watson Studio Review: "IBM DSx Learning Experience"

Score 10 out of 10
Vetted Review
Verified User
Review Source
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)
Read Anferny Chen, MBA's full review
April 13, 2018

IBM Watson Studio Review: "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
March 27, 2018

IBM Watson Studio: "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
March 15, 2018

IBM Watson Studio Review: "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
March 01, 2018

IBM Watson Studio: "dsx_review"

Score 7 out of 10
Vetted Review
Verified User
Review Source
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
February 22, 2018

IBM Watson Studio Review: "DSX for SMEs"

Score 4 out of 10
Vetted Review
Verified User
Review Source
We use it to build predictive models in the certification sector for food. DSX is used by 2 people across disciplines.
  • Collaboration
  • Modelling - Watson services
  • Data Engineering
  • Pricing is not clear
  • Complicated to set up and onboard
  • Too many different IBM products
Big, international projects where price is not an issue. For companies where data is the main issue.
Read Jochen Kleboth's full review
April 16, 2018

IBM Watson Studio Review: "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.
Read this authenticated review
April 06, 2018

IBM Watson Studio Review: "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.
Read this authenticated review
March 21, 2018

IBM Watson Studio Review: "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.
Read this authenticated review

About IBM Watson Studio

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