HPE GreenLake for AI, ML and data analytics
AI, ML and data analytics can transform your organization. Start with a data foundation that enables you to unify, analyze, and act on data everywhere, in a way that’s open, hybrid by design, and with the scale you need. Prepare your AI data with an environment that enables you to develop and deploy more accurate models, with reduced bias. Train and create models at any scale to quickly get results, as you accelerate your data analytics pipeline, and help move AI and ML from experimentation, POCs and pilots, into production and beyond.
AI, ML and data analytics advisory professionals and products are here to help you at any stage of transformation.
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Advancing with AI, ML and data analytics
How do you realize the full potential of AI, ML and data analytics? Let AI and analytics access structured and unstructured data so data users can unlock its value, regardless of where it’s located. Leverage an open-source ecosystem for choice and flexibility for your data analytics tool sets. Simplify and organize AI data and train ML models, giving you the opportunity to deploy AI and ML models, at scale, across hybrid environments.
HPE is here to help you advance every step of the way.
HPE enters AI cloud market
Introducing HPE GreenLake for Large Language Models (LLMs) for any enterprise.
Create your data foundation
Seamlessly access and manage all your data everywhere. Prepare and make your data usable for AI, ML and data analytics, as you drive innovation.
Streamline your training and development
Build analytics pipelines and train AI and ML models faster with an open and heterogeneous architecture.
Scale deployments
Simplify and speed your path from AI and ML POC and production to deployment, at scale.
Power digital transformation from edge to cloud
Organizations in every industry are looking to leverage artificial intelligence (AI) and machine learning (ML) to harness the power of their data and deliver business value. But even when they achieve some measure of success with machine learning pilot programs, many organizations face challenges when they seek to scale these programs to production: lack of data control, data egress costs, security, AI ethics concerns, lack of expertise and deployment flexibility, siloed data and workflows, edge deployments, and daunting operational costs.
