Good for large teams with large data reiterating on their data projects in various steps.
Collaboration is the key aspect in whatever projects we do at our organization. We have people from web development, data science, and ML …
Overview
What is IBM Watson Studio?
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data…
Recent Reviews
Awards
Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards
Popular Features
- Interactive Data Analysis (22)10.0100%
- Visualization (22)10.0100%
- Connect to Multiple Data Sources (22)8.080%
- Extend Existing Data Sources (22)8.080%
Pricing
N/A
Unavailable
What is IBM Watson Studio?
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI…
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Would you like us to let the vendor know that you want pricing?
41 people also want pricing
Alternatives Pricing
What is Mathematica?
Wolfram's flagship product Mathematica is a modern technical computing application featuring a flexible symbolic coding language and a wide array of graphing and data visualization capabilities.
What is Rational BI?
Rational BI provides analytics, data science and business intelligence in an analytical platform that connects to databases, data files and cloud drives including AWS and Azure data sources, enabling users to explore and visualize data. Users can build real-time notebook-style reports directly in a…
Features
Return to navigation
Product Details
- About
- Competitors
- Tech Details
- FAQs
What is IBM Watson Studio?
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI lifecycle management and accelerates time to value with an open, flexible multicloud architecture.
IBM Watson Studio Competitors
IBM Watson Studio Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
Frequently Asked Questions
Amazon SageMaker and Azure Machine Learning are common alternatives for IBM Watson Studio.
Reviewers rate Automatic Data Format Detection and Visualization and Interactive Data Analysis highest, with a score of 10.
The most common users of IBM Watson Studio are from Small Businesses (1-50 employees).
Comparisons
Compare with
Reviews and Ratings
(221)Attribute Ratings
Reviews
(1-25 of 30)Companies can't remove reviews or game the system. Here's why
May 10, 2021
Why Use IBM Waston Studio?
We used IBM Waston for learning and helping other fellow members learn some concepts of machine learning. We learned about IBM Waston through Coursera Specialization and then continue experimenting with IBM Cloud for some time. Whether it is using their services or storing objects in a bucket, it was an amazing experience.
- IBM Watson Services like speech to text, etc. are just some clicks away. You just need to specify some basic details like location etc and the resource will be ready for use.
- IBM DB2 engine is a fully managed relational database for all your needs.
- There are a lot of services available from which users can choose what suits his/her needs.
- In starting, I found navigating through different services a bit difficult and overwhelming.
- IBM dashboard should be redesigned to make it simple.
- Rest all looks good.
April 05, 2021
IBM Watson Studio on Cloud Pak for Data for students
Currently, I am a student and I do not have any idea how many students studying and practicing along with me are using IBM Watson Studio on Cloud Pak for Data. Mostly, this platform might be used by the students under the computer science and information technology department. I use it mostly for my projects by learning to implement several concepts, helping me build and strengthen them.
- Data security
- Choice of the amount of computation power
- Providing an option for sharing the files while hiding the sensitive content present in them
- Checking if it is under use or not because for free users who cannot afford to pay, it is hard to manage the amount of computation periods provided
- When there is nothing to execute, the run time should be paused to prevent wasting resources
- Please try to provide the lite pack with a few more resources to help those who cannot afford to pay
March 30, 2021
IBM Watson Studio on Cloud Pak for Data Review
The IBM Watson Studio is mainly used for one single department, the data science team. It mainly addresses the devops overhead of heavy jupyter notebooks and provides an integrated interface for people who are not familiar with infra and storage. It also provides a point of integration with other IBM services.
- Sharing with team
- GitHub integration
- Free pricing plan if you want to try things out
- Loading times can be slow
- Tabs can be hard to navigate
- not enough out of box examples
February 08, 2021
IBM Watson Studio on Cloud Pak for Data Review
Storing data in the form of Worksheets and CSV files so that multiple users can use the data partially irrespective of location.
Running and deploying ML and AI Models. It helps - no need to have local hardware. We are able to achieve all the tasks over the cloud.
Used in different parts of the organization.
Running and deploying ML and AI Models. It helps - no need to have local hardware. We are able to achieve all the tasks over the cloud.
Used in different parts of the organization.
- Deployment of ML Models.
- Use of sharable data.
- Multiple users can be added to a project.
- UI difficult.
- Use of Microsoft tools like Visio for flow.
- In built Excel editor.
January 04, 2021
Great services for fast and effecient data analytics!
This system is currently being used with a few students on a data science degree within the School of Computing at our University. We are using IBM Watson as a means to overcome the hardware limitations we have within the our work setting. IBM Watson provides student with access to high powered machines allowing them to run complex machine learning algorithms without having to worry about hardware negatively effecting the performance of said algorithms. It is also a relatively simple system to use, making it a useful teaching tool which requires minimal support for academics. Students have provided positive feedback regarding the use of this service and we plan to expand our use of Watson Studio throughout our other degree options.
- Clear distinction between services provided.
- Jack of all trades without being a master of none.
- Complex processing without an major latency.
- Some aspects of the UI can be overwhelming for a novice user.
- Integration with some non-Watson Studio services is limited.
December 01, 2020
Beginner Guide Review
IBM Watson studio is being used to host Juypter Notebooks. These notebooks contains analyses for various projects. The primary project being a ML algorithm that can detect fraud in PPP loans. This is specifically being used in the Engineering department at SJSU. It addresses the problem of users having the capable hardware to run the required software programs, since everything is now cloud based.
- Usability - The Cloud Object Storage is fairly easy to implement with a Juypter Notebook
- Design - After some initial learning curve, it becomes easy to navigate through the site.
- Features - IBM is adding more and more features to their existing architecture.
- Layout - There is a learning curve learning how to navigate the site, it's not as straightforward at it may seem.
- Documentation - There is not that much good documentation available about how to setup various Watson Studio projects (configurations, etc.). Third-party resources provide a set of documentation and guides.
- Bugs - There are some small bugs when using Watson Studio. One issue is when inviting collaborators to a project, depending on which computer you are using and what OS you have as well as your screen size, the invite button is "hidden". The invite button can only be noticed when zooming out far enough.
November 25, 2020
My IBM Watson Studio experience
IBM Watson Studio [formerly IBM Data Science Experience] helps my business unit to make some business decisions concerning management of cash and keeping stocks of debit cards. Generally it help us predict the amount of cash and debit card we would be needing to meet up the demands of the customer at the time.
My organization as a whole use it to predict the profitability of Automated Teller Machines (ATM) and to find insights while there are higher traffic on some machines.
My organization as a whole use it to predict the profitability of Automated Teller Machines (ATM) and to find insights while there are higher traffic on some machines.
- Helps to predict profitability of terminals at any of our locations
- Helps to predict peak and off-peak periods, hence, it aids preparation
- Help us to plan and improve on cash management efficiency by relying of past data
- IBM Watson studio needs to improve on its mobile experience
- A help chatbot would go a long way to guide users
November 24, 2020
My first experience on Watson Studios
Currently I'm using IBM Watson as part of my Professional development via Coursera. IBM Watson to my knowledge isn't being used by my colleagues. I plan on using it to gather data and analytics to better support my customers. As an example I'm a Logistic Assistance Representative with a specialization in Tactical Radio communications thus if I can analysis the Army equipment readiness data and parts requisitioning backlogs I can better pin-point the average turn around time to assist my supported units and the respective parts managers to increase the parts availability and thus improving their readiness.
- Helpful get started tutorial videos in Watson Studios.
- Lots of notebooks to choose from.
- Github push is a great way to share and collaborate with others.
- I'd like to have more CHU's on the lite version.
- When using Python environment it would be great when I make an error coding that corrective suggestions would be available.
- Being very new it's a learning curve maybe a I'm new to coding tutorial class would help.
February 25, 2019
IBM Watson Studio: Ideal for Rapid Data Science and ML POCs and Deployments with Watson
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.
May 08, 2018
Watson vs. DATA
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.
April 16, 2018
Great resource for learning
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
April 13, 2018
Watson Studio opinion
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.
April 06, 2018
DSx, 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.
March 30, 2018
Good Toolset for a Data Scientist
We use the DSX for modeling and prototyping. It is also good for demonstrations and code sharing.
- Good setup for R, Python and Spark/Scala
- Available as desktop version for offline usage
- Easy to handle data sets
- Easy to add new libraries for R and Python
- Sometimes both versions (cloud and desktop) crash
- Small community for backup
- Missing implementation of SPSS
- Free cloud version with low performance
March 27, 2018
My IBM Data Science Experience (DSx) Review
IBM Data Science Experience (DSx) is being used by my university under a Bluemix license.Personally, I used it during a group project in which we addressed a Machine Learning problem using PySpark.
- Quick access to all the features on the dashboard. Good connectivity to the clusters.
- Efficiency for a teamwork and flexible when using shared projects.
- Easy to use from the very start and very flexible platform.
- Sometimes the kernel is slow and that is annoying if you are in need of a quick check of your results.
March 21, 2018
IBM DSx
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.
March 21, 2018
DSX from a BP perspective
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
March 16, 2018
Data science for risk analytics.
We use IBM Data Science Experience across our organisation for all our data engineering work. We are analysing weather risks and produce pricing and risk analysis in Python. We also use stream analytics through Python and SPL. Watson and machine learning are also of great use for us.
- User experience. Easy, fast and user friendly.
- Access to IBM cloud computing power.
- Access to IBM resources and Watson.
- Would like more samples.
- Developing communities.
March 14, 2018
Suitability of DSx for a Data Science Project
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).
March 06, 2018
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
February 23, 2018
DSx - as a beginner
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.
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.
February 22, 2018
DSX: a cloud solution to make data science in the company a reality
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.
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.
February 22, 2018
DSx for Consulting Assessment
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.
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.
February 21, 2018
The Real Data Science Experience
DSx is used for people that want to collaborate on projects concerning Machine Learning and Artificial Intelligence. It was used to create a recommender system on an iPython notebook, a classification solution and currently a genetic algorithm implementation. Overall, the advantages it provides is a stable platform, where users can run online a solution, save the results and collaborate, which seems to be very useful for our organization. It is mostly used by the department of Analytics but the results are viewed and used by managers of all departments. It particularly addresses problems that have to do with the exploding amounts of data and monitoring performance as the user can save and control their data and results.
- DSx has a very straightforward UI, that is simple and easy to use even by users without prior relevant experience.
- DSx has cloud Implementation enabling data scientists and analysts to work on a project collaboratively and store all the data and results they produce on the cloud.
- DSx uses open source solution and brings together the state-of-the-art tools for data science: python and R, on a single platform.
- Another very important advantage is the learning aspect of the platform, as it guides the user with tutorials and good documentation, making it simple to use by non experienced users.
- The kernel of the platform has been quite unstable from time to time causing problems to running code and results.
- The collaborators of a project do not have the option to run code simultaneously on the platform making it difficult to actively achieve collaboration.
- While R and python are the 2 major analytics tools, there are many more that exist and could be implemented to achieve improvements in results and to attract more users with different analytical and software development backgrounds.
February 20, 2018
DSX Review - Customer Loyalty Models
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