Amazon SageMaker vs. IBM watsonx.governance

It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.

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

• Machine Learning at scale by deploying huge amount of training data
• Accelerated data processing for faster outputs and learnings
• Kubernetes integration for containerized deployments
• Creating API endpoints for use by technical users

Incentivized

• The policy management rules are my strongest use case. I set policy rules around our models all the time with no issues
• I was also surprised by how good the metadata enrichment feature is

Incentivized

• It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
• Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

Incentivized

• Needs lot of time initially to setup and get going
• documentation and tutorials are lacking
• pricing is on higher side, which can be an issue for smaller organizations without benefit of scale

Incentivized

Research data can be handled and
governed more effectively to save time and minimize errors. Practical learning helps students
become more marketable to employers by giving them practical experience with
industry-standard tools.
Updates content on AI
governance in courses to make them more appealing to students. Lowers the time
needed to manually check for biases, increasing the validity of research
findings.

Incentivized

Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

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

• We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
• We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
• For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

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