Machine learning (ML) engineers and data scientists are on a never-ending search for new solutions that will enable them to better focus on innovation and accelerate their time to production—and this is what HPE Machine Learning Development Environment is all about.

By removing the complexity and cost associated with ML model development, this comprehensive platform speeds time to value for model developers by:

• Removing the need to write infrastructure code

• Making it easier for IT administrators to set up, manage, secure, and share AI compute clusters

With the HPE Machine Learning Development Environment, ML practitioners can:

• Train models faster using state-of-the-art distributed training, without changing their model code

• Automatically find high-quality models with advanced hyperparameter tuning from the creators of state-of-the-art tuning algorithms such as Hyperband

• Get more from their GPUs with smart scheduling, as well as reduce cloud GPU costs by seamlessly using spot instances

• Track and reproduce their work with experiment tracking that works out of the box, covering code versions, metrics, checkpoints, and hyperparameters

Using a comprehensive array of features integrated into an easy-to-use, high-performance ML environment, ML engineers can focus on building better models, instead of managing IT infrastructure. Using the HPE Machine Learning Development Environment that supports both cloud and on-premises deployment infrastructure, practitioners can develop models using PyTorch, TensorFlow, or Keras. HPE Machine Learning Development Environment also integrates seamlessly with today’s most popular ML tools for data preparation and model deployment.

HPE Machine Learning Development Environment is built upon the widely popular open source training platform, Determined. Check out these related articles about Determined on the HPE Developer portal here.

We also invite you to check out the Documentation for HPE Machine Learning Development Environment.