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…
Why Use IBM Waston Studio?
Brilliant overall cloud product for data storage, processing, and analysis
IBM Watson Studio on Cloud Pak for Data for students
IBM Watson Studio on Cloud Pak for Data Review
IBM Watson Studio on Cloud Pak for Data Review
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Auto AI is a must have for every Data Analyst
Great services for fast and effecient data analytics!
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Awards
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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
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
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Features
Platform Connectivity
Ability to connect to a wide variety of data sources
- 8Connect to Multiple Data Sources(22) Ratings
Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion
- 8Extend Existing Data Sources(22) Ratings
Use R or Python to create custom connectors for any APIs or databases
- 10Automatic Data Format Detection(21) Ratings
Automatic detection of data formats and schemas
- 6.4MDM Integration(14) Ratings
Integration with MDM and metadata dictionaries
Data Exploration
Ability to explore data and develop insights
- 10Visualization(22) Ratings
The product’s support and tooling for analysis and visualization of data.
- 10Interactive Data Analysis(22) Ratings
Ability to analyze data interactively using Python or R Notebooks
Data Preparation
Ability to prepare data for analysis
- 10Interactive Data Cleaning and Enrichment(22) Ratings
Access to visual processors for data wrangling
- 10Data Transformations(21) Ratings
Use visual tools for standard transformations
- 8Data Encryption(20) Ratings
Data encryption to ensure data privacy
- 10Built-in Processors(21) Ratings
Library of processors for data quality checks
Platform Data Modeling
Building predictive data models
- 10Multiple Model Development Languages and Tools(21) Ratings
Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.
- 10Automated Machine Learning(22) Ratings
Tools to help automate algorithm development
- 10Single platform for multiple model development(22) Ratings
Single place to build, validate, deliver, and monitor many different models
- 8Self-Service Model Delivery(20) Ratings
Multiple model delivery modes to comply with existing workflows
Model Deployment
Tools for deploying models into production
- 9Flexible Model Publishing Options(22) Ratings
Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.
- 7Security, Governance, and Cost Controls(22) Ratings
Built-in controls to mitigate compliance and audit risk with user activity tracking
Product Details
- About
- Competitors
- Tech Details
- FAQs
What is IBM Watson Studio?
IBM Watson Studio Competitors
IBM Watson Studio Technical Details
Operating Systems | Unspecified |
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Mobile Application | No |
Frequently Asked Questions
Comparisons
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Reviews and Ratings
(221)Attribute Ratings
Reviews
(1-14 of 14)Great services for fast and effecient data analytics!
- 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.
Watson vs. DATA
- 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.
I used to swear by IBM DSX
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
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.
From the heart
- Intuitive interface, to allow anyone to participate.
- Can use low level coding to improve performance and efficiency.
- Integration towards other watson products.
- Does not seem industry ready, a lot of integration bugs/problems.
- Lack of clear and complete documentation of integration's within own organization.
- Hard to maintain big projects (classes in python files etc).
Good Toolset for a Data Scientist
- 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
- 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.
Pretty good, but still away from perfect
- Integration with Spark
- Jupyter-like environment
- Asset and community access
- Easier access to data
- Connection with on-premise datasources
- Personalization
My thoughts on DSX
- 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.
Seamless environment setup
- Setting up Python environment and Spark. Allowing developers to choose the version of the language
- Getting the credentials automatically to import data.
- Importing CSV data (not at all the same when I tried with json data)
- Nice integration of Python notebooks
- Data visualization - not all data are visualized in a seamless manner (DSX tried to complement Matplotlib, but their tool is not as effective)
- Facilitate developers in integrating DSX output in their own website
- Saving the state of a notebook might help (I understand that python notebook must be re-run when interrupting the kernel, but avoiding to re-run everything might help - especially in long notebooks)
Less Appropriate: integrating graphs. Even if it is possible to use matplot lib in python the data visualization part in IBM DSx has a lot of shortcomings. Maybe because there is not a specific visualization tool associated to it yet. For example, Elastic Search provides Kibana on top of it for the data visualization. Hope this example can be inspiring to make DSx an even greater tool.
DSx - as a beginner
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.
Great for non-programmer analyst
- 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: a cloud solution to make data science in the company a reality
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.
The Real Data Science Experience
- 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.
IBM DSx Experience
- I particularly like working on R for ML problems. DSx provides both Jupyter notebook and R studio interfaces for doing the same. Which is fantastic in terms of flexibility and applicability.
- There are multiple used cases explained in the community section so that one can learn and apply the knowledge at the same time.
- Ease of navigation was of a fantastic magnitude using the easy drop down menus.
- Apache spark connection to R Studio tool keeps on disconnecting. Lot of room for improvement there. A stable connection helps the user have a good experience.
- Many ML functionalities under H2O package in R don't seem to work in the Apache Spark environment.
- If a documentation could be provided regarding ML using Apache Spark in R that would be really helpful.