Azure Machine Learning vs. IBM watsonx.ai

Azure can be a more unified product. It feels like 10 different tech teams were building it but we're not talking to each other. An example is when the user needs to know what is the next step. Automatically saving a previous state is very helpful as new users are usually not aware of the functionality.

Incentivized

For genai apps its very good i can say where we don't have to worry about the whole ecosystem their whole ecosystem is flawless and very powerful analytical capabilities. It maintains the data Quality and data security. When cost is concerned and when there are large data involved. It becomes costly and tuning of model is not straightforward as there is no proper active community for which we can take help

Incentivized

• Easy to create the experiment.
• Easy to adopt the best algorithm.
• Efficient way to deploy the model as a web service.
• Centralized platform for the life cycle of machine learning goal.

• It allows specialists to apply several base models for specific subtasks in the field of NLP.
• Gives the availability of many models developed for AI enhancement for different solutions.
• Has incorporated functionality for data governance and security to support access to AI tools by multiple users.

Incentivized

• Few models: Even though it has a lot of Machine Learning models, it is quite limited when compared to R. Most Data Scientists still use and prefer R, so the newest models tend to release as R libraries. With Azure ML, we need to wait for Microsoft to evaluate and decide if including a new model is a good idea or not
• Tableau interface: last time I checked there was no easy way to connect with Tableau.
• Cloud based: You always need a good internet connection to use it.

Incentivized

• I would love it to provide more low-code or no-code options so we could offer Watsonx to non-developer staff and students instead of ChatGPT or Copilot.
• They should have a natural language interface to the AI Assistant analytics so that there is no need to graph these outside Watson.
• Similarly, the 30 day limit on conversation data is limiting and drives us to build reporting outsdie IBM watsonx.ai.

Incentivized

its a future

Incentivized

Good UX/UI and overall good usability, but it takes a while to get used to the product & platform. The whole design seems fragmented with little in terms of integration with project management tools such as JIRA, or wireframing. Overall it feels like an unfinished product that's meant for teaching more than for production.

Incentivized

I needed some time to understand the different parts of the web UI. It was slightly overwhelming in the beginning. However, after some time, it made sense, and I like the UI now. In terms of functionality, there are many useful features that make your life easy, like jumping to a section and giving me a deployment space to deploy my models easily.

Incentivized

I'm satisfied with the Azure Machine Learning Studio- it fulfilled my goal in a single channel. Even haven't worr[ied] about the maintenance or any fault tolerance. This provide[s] the user interactive UI to grab the features easily. [Their] support teams also very help[ful], they stand with us at any time.

Not sure

The answer is quite simple: Microsoft Azure Machine Learning Workbench is the cheapest and most user friendly analytics tool I have ever seen! Unless you are running a team of data scientists, this is the tool to go. Most functions (marketing, sales, finance, supply chain, logistics, HR, R&D, etc.) could easily integrate Azure ML in its day to day activity.

Incentivized

The use cases of code explanation, code suggestion, code review, and code conversions from one language to another were relatively easy to build in Watson.ai than using CoPilot. I found that the contextualization of code for a packaged solution is easier to do in Watsonx.ai platform during my initial research.

Incentivized

• It is easy to learn and construct, which impacts directly on productivity.
• Good for experimentation and validation for simple models.
• Has a use cost less than the best alternatives in the market.

Incentivized

• Time saving to set up the infrastructure - without watsonx.ai we would have had to set up everything individually
• The first point translates directly into cost savings
• The compliance aspect was a game changer for us and provided us with the confidence to focus all our efforts only on IBM watsonx.ai

Incentivized