IBM Machine Learning for z/OS vs. MLReef

IBM Watson Machine Learning is an AI-based scalable self-learning model for any type of business. It can be used to help any company automate repetitive tasks, predict future trends, and make data-driven decisions. I used it to predict stock prices based on certain variables. It works well, cost me nothing, and gives me the ability to create my own AI-based models that I can use for any purpose.

Incentivized

Works well if you have to involve different roles in different organizations in a project. Less suited when you have a complex system of custom developed tools

• Good machine learning tool
• Easy integration

Incentivized

• Helps us to take on more client projects
• Can be used by data analysts as well as casual users

• Proper usage of REST API documentation is missing.
• Not localization friendly, cannot support regional or local language documents.

Incentivized

• Out of the box support for major cloud vendors

IBM had a hard time providing business level support. There were a lot of data scientists and technology experts but rarely a simple business person shows up. Also the way IBM operates IBM Consulting has competing priorities as compared to IBM Technology. This has resulted in a lot of confusion at the client's end.

Incentivized

We have been using Microsoft Azure as a machine learning tool. But the challenges remain the same. These are all tools that you need a robust analysis before a decision on the tool. Unfortunately, the technology company cannot make that determination due to lack of core business understanding. Without that the project is doomed.

Incentivized

• Create secure business environment.
• Save upto 90% of manual labor.
• Improve my sales and marketing ROI.

Incentivized

• We can handle 4 to 6 times more projects at the same time with our team
• We stay engaged with our customers well beyond the project duration