Transforming AI Workflows with HPE Private Cloud: The Ultimate Toolkit for Innovation

HPE Private Cloud AI provides tools and frameworks for performing Data Engineering, Data Analytics, Data Science, and Generative AI workloads in addition to the NVIDIA AI Enterprise stack. Here are some of the tools to aid the development of AI workloads:

1. HPE Portfolio Assistant: It’s a chatbot that assists users in learning more about HPE’s products. It provides an interactive search interface that helps users explore and navigate HPE’s product portfolio. The tool also provides personalized recommendations based on the user’s requirements. In addition to the basic product features, detailed specifications, benefits and various case studies of HPE offerings will also be available. It helps various stakeholders like IT decision-makers, partners, and sales teams to select the right infrastructure, recommend appropriate products, and enhance product knowledge.

2. Feast (Feature Store for Machine Learning): It’s an open-source feature store that acts like a central repository for storing and managing features. When there is a team of data scientists involved in a project, it will be difficult to maintain a central hub to store the features. This problem can be solved with the help of Feast, where data engineers, data scientists, and machine learning engineers can collaborate with ease when it comes to sharing the features across the projects. It supports online and offline feature retrieval options. Features could be shared consistently across the data pipeline, model development, and inferencing. It supports feature versioning and auditing as well to improve transparency.

3. Kubeflow: It’s an open-source platform used for developing and deploying Machine Learning models. It provides scalability by scaling the ML models across multiple nodes using Kubernetes. It has a pipeline to automate the orchestration of ML workflows with reusable components. KFServing component enables scalable model serving and inference. Katib supports hyperparameter tuning and model optimization. Distributed training of TensorFlow and PyTorch models could be done with the help of TFJob.

4. Ray: It’s an open-source framework for distributed computing that is designed to scale AI, ML, and Python-based applications effortlessly. It supports parallel and distributed execution across multiple nodes. The Ray Core component enables distributed task execution and parallel processing. Ray Tune could be used to perform hyperparameter tuning. Then there is a Ray Serve component which provides a flexible and scalable platform for model deployment. Ray RLlib component offers tools for distributed reinforcement learning. Ray can be easily integrated with ML frameworks like TensorFlow, PyTorch, and Scikit-learn.
5. HPE MLDE: MLDE refers to Machine Learning Development Environment. It supports development of end-to-end machine learning model development and deployment. It supports distributed model training and large-scale ML workloads. There is Automated Machine Learning Pipelines available to streamline the process of creating, training and deploying models. It also supports version control and monitoring. MLDE can be easily integrated with HPE’s AI infrastructure, including HPE Ezmeral and HPE hardware. It can be easily integrated with popular ML frameworks such as TensorFlow, PyTorch, and Scikit-learn and supports integration with Kubernetes for orchestration and Ray for distributed training
6. Mlfow: This is one of the best open-source frameworks to support model tracking. In addition to model tracking, mlflow can also be used experimentation and deployment of ML models. The MLflow Tracking component allows to log and record ML experiments including code, data and model parameters. MLflow Projects could be used to package ML code for reproducibility. Mlfow models support model deployment across multiple cloud platforms. The mlflow registry is a centralized registry used for storing and managing model versions and metadata.

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