IBM Machine Learning for z/OS vs. Keras

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

I would recommend it for use when anyone wants to quickly develop a neural network. Or if a user is solving any machine learning problem that includes deep learning. And this kind of problem will be like image recognition, face recognition, doing some text analysis using deep learning which includes LSTM or some other algorithm.

Incentivized

• Good machine learning tool
• Easy integration

Incentivized

• Implementing neural networks and deep learning models is easy with this.
• Data processing is easy with Python and Keras. Keras helps a lot and has a good collection of functions to do data processing.
• It has good integration with other devices like Android.

Incentivized

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

Incentivized

• I didn't face any issue so far.
• The only thing, you can't modify everything in this. So it's not recommended for constructing highly optimised algorithms.

The reason for giving this much rating. 1. It makes my job really easy and fast. 2. Strong community support. 3. Overall cost.

Incentivized

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

Keras have really good support along with the strong community over the internet. So in case you stuck, It won't so hard to get out from it.

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

As Keras is the high level API, so using Keras, we don't have to be bothered by the low level TensorFlow complexity, and we can reduce a lot coding and testing efforts.

Incentivized

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

Incentivized

• It helped me in learning the basic concept of deep learning by having hands-on experience.
• It has helped us to implement our NN with very little time.
• It doesn't give you the whole power to customize your neural network. If you want that then you have to shift to TensorFLow

Incentivized