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.
Collaboration is the key aspect in whatever projects we do at our organization. We have people from web development, data science, and ML teams working on projects involving all three domains in a single project. IBM Watson Studio on Cloud Pak for Data is a great asset for the data science team where we work on individual tasks, collaborate with teammates, and share findings and insights. It has all the basic tools required for visualization and modeling the algorithm. The IBM cloud Pak has numerous examples for various use cases.
Pros
The R studio which is very flexible more than any IDE.
Testing and developing the model locally before finally publishing.
Collaborating with teammates on same data.
Cons
Watson Studio is a little complex for beginners to get started. Paid courses explain them well to beginners.
Many features that small teams might not use.
Likelihood to Recommend
It has a lot of features that are good for teams working on large-scale projects and continuously developing and reiterating their data project models. Really helpful when dealing with large data. It is a kind of one-stop solution for all data science tasks like visualization, cleaning, analyzing data, and developing models but small teams might find a lot of features unuseful.
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.
IBM Watson Studio on Cloud Pak for Data helps me bring in multiple data streams in batch and streaming mode and helps me to run ETL processes and then run ML algorithms on top of the processed data. The beauty is I don't need to think about managing resources like CPU, storage, and processing elements and focus all my efforts on the data analytics.
Pros
Data ingestion
ETL processes
Integration with Python notebooks for ML algorithms
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
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.
Pros
Sharing with team
Github integration
Free pricing plan if you want to try things out
Cons
Loading times can be slow
Tabs can be hard to navigate
not enough out of box examples
Likelihood to Recommend
IBM Watson studio on Cloud Pak for Data is well suited for medium sized teams. It allows for collaboration between technical and non-technical users. It is less suited for companies who already has large built production ML pipelines, as the cost of migration could be high and the initial overhead of learning the tools still remains
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.
Pros
Deployment of ML Models.
Use of sharable data.
Multiple users can be added to a project.
Cons
UI difficult.
Use of Microsoft tools like Visio for flow.
In built Excel editor.
Likelihood to Recommend
I am not sure whether can use Watson for Robotic process automation. I like the ease of usage of Watson for ML Models and Image processing. I love the way the Project is associated with Assets. I like the different data connectors.
We mostly use IBM Watson Studio for its Notebook features for running Python codes. It allows us to work on the code together and generate neat reports.
Pros
Jupyter Notebook
Easy to share
Looks good
Cons
Kind of slow to launch
Not industry standard
Too many tabs to navigate through
Likelihood to Recommend
If you are a small company with insufficient funding, it would be better to go with open source resources because they are free and do the same job. If you are a bigger company with enough resources, then IBM Watson Studio could be better in terms of security and accessibility.
VU
Verified User
Analyst in Research & Development (1-10 employees)
Used to test prototype applications for clients. Mostly used for creating predictive data models, descriptive models, and basic ETL. There are plans to test new speech-to-text and image-to-text applications for new clients in 2021. Cloud storage is a secondary use since it is the only platform that supports older or legacy databases
Pros
Auto AI makes creating predictive models so much easier and faster. It creates several models and ranks them according to precision (or accuracy) allowing us to rapidly select the most optimized model. While the models are not perfect at the first run, it gives us an idea on which models to focus on cutting the turnaround times from 3 days to less than 4 hours.
The cloud structure allows us to reuse datasets that are in different projects. This cuts down the need to create new pipelines or ETL steps.
Cons
Auto AI allows us to select the best models to use when creating predictive models. The app ranks and lists down the models according to accuracy (or precision0. This alone is worth the subscription as it cut down our turnaround times from 3 days to 1 day.
Likelihood to Recommend
It is well suited for mechanistic models such as time series and descriptive analysis. There is almost no code necessary when creating these models, as it takes the guesswork out of setting up the parameters. There are also models that we've never before even tried because of its complexities but AutoAI shows that these models are sometimes best for the given problem.
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.
Pros
Clear distinction between services provided.
Jack of all trades without being a master of none.
Complex processing without an major latency.
Cons
Some aspects of the UI can be overwhelming for a novice user.
Integration with some non-Watson Studio services is limited.
Likelihood to Recommend
IBM Watson Studio is very much suitable for data scientists when running a variety of analytical models using various languages such as R, Python and Scala. If you are planning to use data science driven languages in a cloud setting then IBM Watson Studio is a good option as it combines lots of relevant tools such as Notebooks, RStudio and Spark in a single environment. If you are looking to work in these environments as a group then Watson Studio also works well with the distribution and sharing of workspaces. This service however, isn't always the best solution as it can become costly if you are consistently running a large amount of intensive projects.
VU
Verified User
Employee in Information Technology (1001-5000 employees)