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 …
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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…
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- 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%
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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…
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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 Enterprises (1,001+ employees).
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Reviews
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March 05, 2018
Seamless environment setup
IBM Data Science Experience is used in my lab to get insights from data. We have a grant with my company to use IBM services and I am very happy for it. In my lab, we are targeting human-computer interaction and trying to extract user's behaviors from data. We have small amounts of data. Nonetheless, IBM DSx is a great tool to investigate them. In fact, it avoids the setup of Python and Spark, all the cumbersome settings are done on the cloud so data scientists can focus on the analysis. I believe the setup provided on IBM Data Science is a major "pro" for using the platform.
- 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)
March 01, 2018
dsx_review
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.
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
Great for non-programmer analyst
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.
February 22, 2018
DSX for SMEs
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
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
February 15, 2018
IBM Data Science Experience (DSx) - a ML tool easy to use even for non-technical experts.
In some use-cases, we use it within my research group. More importantly, we used it to win a nation-wide challenge for IoT-based Smart Cities in one of the Nordic countries. In particular, we used it to train various types of classifiers to predict a large number of events occurring in a Smart City scenario.
- Easy to use and well thought UI and UX.
- Ease of use of the entire system. All the phases (from data loading to classifier training to prediction) are easily accessible and well structured/phased.
- Satisfactory prediction results with Gradient Boosting classifier.
- The help and documentation are easy to read and are an easy overview of the steps and how they are interconnected.
- Not easy to use/understand how "Continuous Classifier Performance Monitoring" - we were unable to get it working or fully understand what are the inputs and how to make it work.
January 30, 2018
IBM DSx Experience
DSx was used as a test environment in my organization for applying advanced analytical techniques like machine learning, parallel processing etc. It was used by the analytics department solely and not across the whole organization.
- 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.
January 26, 2018
Welcome to DSx ... DSx is welcomed by us!
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 ...
January 26, 2018
IBM DSx Learning Experience
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.
January 25, 2018
DSx Review
My organisation uses IBM Data Science Experience (DSx) as part of a neural networks series of projects to learn about new tools and trends and to create valuable insights. It was mostly used to implement simple machine learning models.
- Powerful set of tools that are the initial foundation for data science
- The ability to use open-source languages
- Provides a space for you to collect and share notebooks, connect to data sources, and add datasets all in one place
- I'd like to see a bit more done around security and version control
- I'd like to see a bit more done around Machine Learning
- Possibly provide contributors communication tools in the same environment
January 23, 2018
Cooking With Gas - IBM DSx Is Your New Wave Igniter Fluid
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.