IBM Watson Studio

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.

Well suited for

• Data Storage
• Data Warehousing
• Data Ingestion
• Data Analysis using Python

Less Suited for things like

• Streaming Data
• ETL tools area
• AutoML algorithms out of the box
• NoSQL Database support

It provides a lot of professional services which are not provided by other platforms

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

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.

Watson is good when you do not have applications and software locally variable on your laptop or PC.

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.

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.

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.

IBM Watson Studio (formerly IBM Data Science Experience) is very useful for using different analysis techniques to import and interpret data sets. Converting and cleaning data is easy using IBM Watson Studio (formerly IBM Data Science Experience), so it is very helpful in scenarios where you know what you're trying to achieve. From an educational standpoint, it would be nice if the platform offered more instance-related material, but the platform is very useful for experienced analysis.

The IBM Watson Studio is suited for everyone in the organization including a data scientist (who can do the in-depth coding), to a business analyst. Especially when the organizations have to analyze the unstructured data. It has the flexibility in the use of different data science development environments including R, Python and SPSS.

Watson Studio is suited for quick data validation checks. It's fairly simple to upload data resources and to get something up and running.
(Less Appropriate )I have not seen or had experience with Watson Studio services that can handle a large amount of data.

It is well suited for building applications using AI,ML etc..

This depends on your application. I believe IBM Watson Studio [(formerly IBM Data Science Experience)] is agile enough to carry out most of my basic business intelligence tasks

The sheer amount of services from DB to many others is more than one would expect especially being a lite user.
The assumption that every user is well versed in coding or just maneuvering around in Watson studio is definitely no true. Thus having said that a process of instruction or maybe a better tutorial might be appropriate.

Watson Studio is great for visualization and model computing, however, it doesn’t have error handling capability inbuilt.

Watson Studio is optimal for experienced data scientists and machine learning professionals to develop and deploy models quickly while enforcing best practices that set up projects for deployment and management down the road. It's not appropriate for people without a data science or machine learning background for production use; the ease of the visual modelers does not mean it makes machine learning easy or intuitive.

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

Well suited for my organization's claim diagnosis level analysis across the years. Less suited for lower level data analysis which does not add much value.

Well suited for:
1. Offering ancillary upgrades to airline passengers
2. Predicting flight delays based on historical patterns coupled with live feeds like weather

Well suited for cloud-based environments, and less suited for edge-based processing.

It offers an end to end solution for data science analytics

If a company is well resourced, I would recommend it as it allows the software developers to build products quickly and test them, on top of the many features available for advanced users to try. If a company is less well resourced, then it may be more challenging and in this case, the total TCO needs to be analyzed.

We would highly recommend Watson Studio to any of our colleagues interested in combining machine learning and data management due to its wide range of capabilities and ease of use.

Abundant use cases in the community section are very helpful.
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.