Azure Machine Learning vs. IBM watsonx.governance

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

We have been able to make the right decisions based on performance metrics. Data assets across the enterprise have experienced significant growth from comprehensive audits that drive quality growth. The platform has filtered out poorly analyzed data from the workflow chain and introduced stable control mechanisms that meet compliance policies.

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.

• Identify and notify about potential bias in model
• Helps with explainable AI - which in turn helps promote models into production quicker
• Monitors models and provides a framework for model governance

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

• Connecting with tools outside Ibm, we tried once, can be challenging
• Real time can be quicker although it works great
• Perhaps interface, if we count it.

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

We have advanced the marketing base with AI model that have enhanced effective use of the available resources. The integration of IBM watsonx.governance with other marketing systems has streamlined workflows and enhanced compliance. We have fully complied with set market regulations in AI data models that has saved the organization from unnecessary non-compliance penalties.

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

With its smooth integrations with different AI models and strong compliance tools, IBM watsonx.governance leads in comprehensive data governance. IBM watsonx.governance provides a well-balanced combination of governance, compliance, and integration capabilities in contrast to Dataiku, which concentrates more on data science workflows, and Holistic AI, which stresses AI ethics and risk management. That was my choice because of its robust integration features and comprehensive approach.

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

• It has massively cut down the time our compliance teams spent on preparing compliance packs for EU emissions report. We're talking 4 weeks of manual tracing and spreadsheet validations to just under 3 days now!
• IBM watsonx.governance flags anomalies in shipping data 2 weeks earlier than our older system, saving us thousands by renegotiating contracts before spot prices rise

Incentivized