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.
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SAS Viya
Score 6.8 out of 10
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An end-to-end platform for AI, data science, and analytics, used for modeling, as well as management and deployment of AI models.
Google Cloud may be a good place but it is not as easy to understand as IBM Watson is. Google Cloud has a lot of things and it is terrifying for a beginner. You need hours of specialization for that. On other hand, anyone can start using IBM Waston just by the following …
AWS Sagemaker is a well-established product that supports on-demand notebooks, data pipelines, and so on, however, it also comes with the learning overhead of the whole AWS stack. It does allow per-defined models, but the benefit of using IBM Watson Studio is that users are …
Organization of data, use of data, manage the data, visualize the data is easy.
Use of the environment for any project.
We can use python or R or Scala in the notebook.
Easy to use, but still requires a lot of coding to use. There is no ranking of models used and models are not persistent, which means you have to keep running the models again every time you leave the session. The filesystem is clunky and need to keep authorizing Google Drive …
The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and …
IBM offers a deep neural network training workflow, with a flow editor interface similar to the one used in Azure ML Studio. However, the custom build modeling in IBM has notebooks such as Jupiter to program models manually using popular frameworks like TensorFlow, …
With my experience on Jupyter Notebook I think both are good and currently more comfortable with Watson Studio product. With Jupyter it's open source (free) is always good. "Lots of languages (50), data visualization with Seaborn, work with the building blocks in a flexible and …
As an IBM Business Partner, we are financially incentivized to recommend and deploy IBM solutions where it makes sense to do so for the customer. Against other solutions, few have the governance and security that IBM offers, which is essential for any kind of work in highly …
Watson Studio was our choice in data management because its "all-in-one" packaging. Watson studio also stood out to us because it was more affordable and free for our organization to try out. We also greatly value the open source ecosystem Watson Studio has fostered.
Lecturer (Software and Information Technology Council)
Chose IBM Watson Studio
AWS Sagemaker is new, and I personally think it's better than sliced bread. There's very little set up to do. Watson Studio needs to up its game against Sagemaker.
AWS stacks up very favourably against Watson Studio, and in fact this is what the customer ultimately chose over Watson Studio after an evaluation period due to the sophistication, maturity, security, and capabilities of the AWS components. The downsides of AWS are having to …
The learning curve for DSX is smaller compared to other tools. The data science user base often has preferred tools that they have used previously which are often not DSX which makes adoption of DSX by trained data scientists harder than new users.
IBM DSx is more comprehensive and easy to use, IBM Data science experience has many connectors to the data source and guarantees the portability with your old projects.
SAS is just as good as these tools but is pricier. I like that it handles data visualization and modeling together in one platform that's a novel mechanism that is fairly rare. Also, it's forecasting capabilities are nicely integrated with the functionality overall which makes …
We had major use of SAS in forecasting where it doesn't require high level of coding knowledge and which has highly efficient models built in which can give good results on forecasts without lot of manual intervention. This tool was designed specifically for forecasting and …
SAS is faster then both SPSS and STATA. SAS also has better models and graphs when comparing the three softwares. However, STATA and SPSS are more user friendly. It is easy to use SPSS and STATA, because a lot of it is point-click. SAS requires some training to be able to use …
Director, Application Architecture and Programming
Chose SAS Viya
SAS has a much superior and comprehensive data preparation capability with a clear approach on how to handle and scale for a large amount of data and users. However, it can be more expensive to implement.
R is of course much cheaper (free) than SAS Analytics, and it can do everything SAS Analytics can do and more. It is a much more technical tool than SAS Analytics, which is why some people prefer SAS Analytics.
SAS was the incumbent tool, and what the team knew. We did look into using Revolution Analytics enterprise version of R, but the learning curve on that caused us to stick with SAS. In my current position, I've opted for WPS over SAS. I can still leverage my SAS experience, but …
SAS allows the user a wider range of capabilities to cleanse and manipulate the data. Not only can the data be pulled directly into SAS, but before it is finalized it can be transposed, graphed, or altered in any way imaginable which puts it a step above the Business Objects …
Features
IBM Watson Studio on Cloud Pak for Data
SAS Viya
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
IBM Watson Studio on Cloud Pak for Data
8.1
Ratings
3% below category average
SAS Viya
-
Ratings
Connect to Multiple Data Sources
8.00 Ratings
00 Ratings
Extend Existing Data Sources
8.00 Ratings
00 Ratings
Automatic Data Format Detection
10.00 Ratings
00 Ratings
MDM Integration
6.40 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
IBM Watson Studio on Cloud Pak for Data
10.0
Ratings
18% above category average
SAS Viya
-
Ratings
Visualization
10.00 Ratings
00 Ratings
Interactive Data Analysis
10.00 Ratings
00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
IBM Watson Studio on Cloud Pak for Data
9.5
Ratings
15% above category average
SAS Viya
-
Ratings
Interactive Data Cleaning and Enrichment
10.00 Ratings
00 Ratings
Data Transformations
10.00 Ratings
00 Ratings
Data Encryption
8.00 Ratings
00 Ratings
Built-in Processors
10.00 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
IBM Watson Studio on Cloud Pak for Data
9.5
Ratings
13% above category average
SAS Viya
-
Ratings
Multiple Model Development Languages and Tools
10.00 Ratings
00 Ratings
Automated Machine Learning
10.00 Ratings
00 Ratings
Single platform for multiple model development
10.00 Ratings
00 Ratings
Self-Service Model Delivery
8.00 Ratings
00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
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 piloted SAS AA at my organization to see how well it compares with other free software tools such as RStudio and Anaconda. So far what we saw was very impressive especially with the visual display but was a little out of our price range. It would be useful in analyzing population health metrics combined with financial data.
SAS Analytics does not have very good graphic capabilities. Their advanced graphics packages are expensive, and still not very appealing or intuitive to customize.
SAS Analytics is not as up-to-date when it comes to advanced analytical techniques as R or other open-source analytics packages.
Not only does SAS become easier to use as the user gets more familiar with its capabilities, but the customer service is excellent. Any issues with SAS and their technical team is either contacting the user via email, chat, text, WebEx, or phone. They have power users that have years of experience with SAS there to help with any issue.
If SAS Enterprise Guide is utilized any beginning user will be able to shorten the learning curve. This is allow the user a plethora of basic capabilities until they can utilize coding to expand their needs in manipulating and presenting data. SAS is also dedicated to expanding this environment so it is ever growing.
SAS probably has the most market saturation out of all of the analytics software worldwide. They are in every industry and they are knowledgable about every industry. They are always available to take questions, solve issues, and discuss a company's needs. A company that buys SAS software has a dedicated representative that is there for all of their needs.
Although nothing is perfect, SAS is almost there. The software can handle billions of rows of data without a glitch and runs at a quick pace regardless of what the user wants to perform. SAS products are made to handle data so performance is of their utmost important. The software is created to run things as efficiently as SAS software can to maximize performance.
I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
SAS is generally known for good support that's one of the main reasons to justify the cost of having SAS licenses within our organization is knowing that customer support is just a quick phone call away. I've usually had good experiences with the SAS customer support team it's one of the ways in which the company stands out in my view.
SAS has regional and national conferences that are dedicated to expanding users' knowledge of the software and showing them what changes and additions they are making to the software. There are user groups in most of the major cities that also provide multi-day seminars that focus on specific topics for education. If online training isn't the best way for the user, there is ample in-person training available.
There are online videos, live classes, and resource material which makes training very easy to access. However, nothing is circumstantial so applying your training can get tricky if the user is performing complex tasks. When purchasing software, SAS will also allocate education credits so the user(s) can access classes and material online to help expand their knowledge.
Ask as many questions you can before the install to understand the process. Since a third party does the installation your company is sort of a passanger and it is easy to get lost in the process. It also helps to have all users and IT support involved in the install to help increase the knowledge as to how SAS runs and what it needs to perform correctly.
The main reason I personally changed over from Azure ML Studio is because it lacked any support for significant custom modelling with packages and services such as TensorFlow, scikit-learn, Microsoft Cognitive Toolkit and Spark ML. IBM Watson Studio provides these services and does so in a well integrated and easy to use fashion making it a preferable service over the other services that I have personally used.
SAS is faster then both SPSS and STATA. SAS also has better models and graphs when comparing the three softwares. However, STATA and SPSS are more user friendly. It is easy to use SPSS and STATA, because a lot of it is point-click. SAS requires some training to be able to use it as effectively as possible. SAS is better with large data sets, and it is easier to analyze many data points at the same time
It all depends on the type of SAS product the user has. Scaleability differs from product to product, and if the user has SAS Office Analytics the scaleability is quite robust. This software will satisfy the majority of the company's analytic needs for years to come. In addition, if SAS is not meeting the users needs the company can easily find SAS solutions that will.