Amazon SageMaker vs. IBM Cloud Pak for Data

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

IBM Cloud Pak for Data with Netezza is well suited for clients who require fast, economical analytics processing. It is not designed to be used as a transactional processing environment. For example, a large customer is using it during the point of sale process. That makes little sense in that business case. However, to take analysis to market faster, it excels well in that space.

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

• I really like the AI and ML which enables us to source data in different sources for easy data-driven decisions.
• It's a cloud tool that keeps all our data safe, backed up ahs obtainable at any time without being exposed to any kind of risks or loss.
• I like the fact that ICP is main based on open source stack which adds value to products like VA or MCM.
• IBM support service is great and top-class.

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

• This offering is currently available on prem, in Azure, and soon in AWS. GCP availability will be in the future as there is demand in the marketplace
• The on premise offering starts with a Base + 0 model, which is a significant appliance. There are no 'mini' offerings as there were in the past.
• At Destiny, we work closely with IBM to help our clients perform budgetary planning.

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

Generally this tool has been very helpful and innovative because increase our workflow and collaboration using integrated multi-cloud platform. It also enables us to deploy in any flexible way like on-premises or cloud which saves time and hard disk space. It also enables us to connect, catalog, govern, transform and analyze data regardless of the area.

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

• IBM Cloud helps us to manage data speed across every distributed stores and clouds.
• Acts as a single unified tool which brings all our data in one place where it's safe and easy to access.
• Enables all of our data users to collaborate from a single, unified interface that supports many services that are designed to work seamless.

Incentivized