TrustRadius Insights for IBM Watson Studio on Cloud Pak for Data are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.
Pros
Flexibility Appreciation: Users have expressed admiration for the flexibility offered by R Studio, noting it as superior to other available IDEs on the market. They appreciate the versatility that enables them to customize their work environment according to their specific needs, leading to a more tailored and efficient workflow.
Local Model Development Benefit: Reviewers find significant value in the ability to test and refine models locally before final deployment, facilitating more effective model optimization. This feature empowers users to iterate on their models without constraints, ultimately resulting in higher accuracy and better performance when deployed.
Collaborative Data Work Highlight: The platform's feature that allows seamless collaboration among team members working on the same dataset is highly valued by users. By enabling real-time data sharing and simultaneous editing capabilities, teams can work together efficiently towards common goals, enhancing overall productivity and teamwork.
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.
Pros
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.
Cons
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.
Likelihood to Recommend
IBM Waston Studio is well suited if you wanna use some well-known services without investing much of your time there. There are a lot of services that can be used and experimented with. These services are just a few clicks away. Also, there is a free plan if you want to try before actually using the product.
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.
Pros
Data security
Choice of the amount of computation power
Providing an option for sharing the files while hiding the sensitive content present in them
Cons
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
Likelihood to Recommend
It provides a lot of professional services which are not provided by other platforms
VU
Verified User
Engineer in Engineering (Information Technology & Services company, 5001-10,000 employees)
I have been using IBM Watson [Studio (formerly IBM Data Science Experience)] for the purpose of Data science course which was offered by IBM on coursera. As part of that I had come across IBM Watson . It helped a lot for learners who want to do things practically . But I felt that the interface can be made much better so that people can use it in a more flexible way . Also some times there are issues when using jupyter notebooks with python . I felt that can be improved for better user experience
Pros
More variety of applications
More Flexible
Better compatibility
Cons
User Experience
Functionality issues with some applications
Speed
Likelihood to Recommend
It is well suited for building applications using AI,ML etc..
VU
Verified User
Engineer in Engineering (E-Learning company, 11-50 employees)
We are working to leverage data analytics using an on-premises deployment to aid in predicting faults for our customers in a proactive/reactive manner. We are looking to leverage efficient and regularly trained algorithms in our Diagnostics Engine/BPMS to reduce our overall time to handle and potentially eliminate tickets opened by our customers
Pros
Ease of use and quick to explore
Guided experiences and ability to leverage multiple algorithms to identify the best one
Great support and sales teams
Cons
There isn’t much I think I can provide critical or improvement feedback on
Likelihood to Recommend
The guided nature of the front end of DSX/WS truly enable an “easy-button” for casual business analysts/scientist, and the advanced functionality using SPSS is a fantastic blend if easy/advanced AI/Statistics
VU
Verified User
Director in Engineering (Telecommunications company, 10,001+ employees)
Currently it is me and two other people in my organization using it. We use it for internal projects to learn the ropes, then the long term goal is to assist customers in a consultant role to improve business processes. In the current state, the product has a lot of bugs, and does not really solve any business problems. We are using the Apache Hadoop integration towards Watson Analytics Engine and hive tables on the IBM cloud object storage.
Pros
Intuitive interface, to allow anyone to participate.
Can use low level coding to improve performance and efficiency.
Integration towards other watson products.
Cons
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).
Likelihood to Recommend
When a team of hardcore coders and BI people need to work together, then IBM Watson Studio is an ok fit. Visualize data and sharing of notebooks between people at different levels.
VU
Verified User
Engineer in Engineering (Education Management company, 11-50 employees)
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.
Pros
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.
Cons
Sometimes the kernel is slow and that is annoying if you are in need of a quick check of your results.
Likelihood to Recommend
I think it is very useful when dealing with problems which need a parallel computing running environment with multi-clusters.
I found it very comfortable using the notebooks.
VU
Verified User
Engineer in Engineering (Computer Software company, 1001-5000 employees)
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.
Pros
User experience. Easy, fast and user friendly.
Access to IBM cloud computing power.
Access to IBM resources and Watson.
Cons
Would like more samples.
Developing communities.
Likelihood to Recommend
It is very well suited for Python, Rbooks and analysis. It is well linked to other services.
VU
Verified User
Director in Engineering (Insurance company, 1-10 employees)
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.
Pros
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
Cons
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)
Likelihood to Recommend
Best suited: Analyzing great amount of data on a distributed cloud platform - manipulating data is easy thanks to all the setup done by DSX 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.
VU
Verified User
Engineer in Engineering (Research company, 1001-5000 employees)
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.
Pros
Easy project creation.
Large amount of communication resource & example.
Cons
Support of python 3 will be nice.
Notebook crashes quite often.
Likelihood to Recommend
General user experience is good, but the data loading from IBM DB2 is very slow initially.