PowerAI is an IBM Cognitive Systems offering for the rapidly growing and quickly evolving artificial intelligence (AI) category of deep learning.  PowerAI brings a suite of capabilities from the open source community and combines them into a single enterprise distribution of software that incorporates complete lifecycle management from installation and configuration; data ingest and preparation; building, optimizing, and training the model; to inference; testing; and moving the model into production.

Deep learning is the fastest growing subcategory of machine learning and uses software neural networks to help develop patterns of analysis within the system to generate predictive capability: deep learning is a platform that is capable of effectively learning how to learn, and it is immensely powerful for helping clients get the most out of their data.

IBM PowerAI V1.5 is a software solution designed to provide an end-to-end deep learning platform for data scientists. This solution simplifies the process of creating deep learning environments, from single systems to the largest clusters, and provides prominent levels of deep learning training performance by taking advantage of IBM’s unique accelerated systems architecture.

Here are 5 things to know about IBM PowerAI:

1.  Helps to make deep learning easier and faster for organizations.

The solution provides workflow support for the deep learning lifecycle on a distributed architecture from data ingest and preparation, through model training and optimization to inference, moving the model into production. PowerAI helps enable your clients' teams to iterate through the deep learning cycle faster, training on more data to continuously improve the model.

2. Designed to provide an end-to-end deep learning platform for data scientist.

PowerAI offers many optimizations that can ease installation and management, and can accelerate performance, for example:

• Ready-to-use deep learning frameworks (TensorFlow, IBM Caffe, and BVLC Caffe).
• Distributed as easy-to-install binaries.
• Includes all dependencies and libraries.
• Easy updates: Code updates arrive from a repository.
• Validated deep learning platform with each release.
• Dedicated support teams for deep learning.
• Designed for enterprise scale with multisystem cluster performance and large memory support

3. Designed for enterprise scale.

PowerAI is designed for enterprise scale, with software optimized for both single server and cluster deep learning training.  PowerAI takes advantage of a distributed architecture to help enable your teams to quickly iterate through the training cycle on more data to help continuously improve the model over time.  PowerAI enables clients to distribute the training of a model across many servers, with the potential for greatly improving performance. What used to take weeks on a single server can potentially now be completed in just hours. This distributed capability is also transparent to the application logic previously written for a single server implementation. It is the best of both worlds: potential performance improvements without having to change the application code.

4. Deep learning to unleash new analytic capabilities.

Deep learning or machine learning design enables computers to learn from experiences, like how humans learn. In addition, like how humans store past experiences in their brain, deep learning design enables computers to store past experiences as data. Organizations use deep learning to develop powerful new analytic capabilities that span multiple usage patterns, from computer vision and object detection, to improved human computer interaction through natural language processing, to very sophisticated anomaly detection capabilities. At the core of any use case associated with deep learning are very sophisticated pattern recognition and classification capabilities, which serve as the springboard for revolutionary applications and insights of the future.

5.  Training neural network models.

Training a model takes a long time. Training neural network models is often measured in days or even weeks. During this time, any interruption means starting over. In addition, if accuracies are not high, the result might have limited usage and is only known after the completion of the training cycle. With PowerAI, data scientists have visual tools for understanding accuracy while the model is running; If accuracy is not high, the model can be stopped without wasting additional time.

IBM intends to deliver IBM PowerAI Vision, an application development tool for computer vision workloads. IBM PowerAI Vision is intended to automatically train deep learning models for different image and video input data sets.

To learn more about IBM Power AI, refer to the following website:

https://www.ibm.com/us-en/marketplace/deep-learning-platform

Bing He

Bruno C. Faria

Alfonso Jara

Chris Parsons

Shota Tsukamoto

Richard Wale

Dino Quintero