What is ML Ops?
As one unit, the container can easily be moved and run on any operating system in any context. Machine learning operations (ML Ops) is a standardized set of best practices and tools developed to make it easier to design, build, deploy and maintain machine learning models in production. Through the use of automation, ML Ops enables data scientists to unify the release cycle for software, automate testing of ML artifacts, and apply agile principles to ML projects in a disciplined manner, contributing to higher quality models.
What is ML Ops used for?
Data scientists, software engineers, and IT operations professionals use ML Ops to standardize and automate the design, building, deployment, and management of AI and ML models. A rigorous ML Ops approach enables participants to collaborate effectively, implement continuous integration and deployment (CI/CD), and accelerate the pace of development and production.
Why do I need ML Ops?
Machine learning and artificial intelligence can be notoriously difficult to put into production. Data science is frequently complicated by silos, conflicting formats, privacy issues, security requirements, and lack of resources. ML Ops can help streamline the development, testing, and release process for data science workflows, bringing speed and agility to challenging AI and ML projects.
Features and benefits of ML Ops
ML Ops can vary in scope from exploratory data analysis (EDA) to data prep and engineering to model training and deployment. Properly applied, it makes ML workflows fast, efficient, and repeatable, accelerating time to production.
ML Ops enables data science teams to develop and deliver higher-quality models more quickly. It dramatically improves management and scalability: multiple models can be controlled and monitored in parallel, permitting continuous integration, delivery, and deployment. And it encourages collaboration between data scientists, DevOps, and IT operations, reducing friction between teams with occasionally conflicting priorities.
ML Ops also minimizes development risks, addressing security and regulatory concerns with rigorous compliance and transparency. Every change to models and data is meticulously tracked to ensure precise auditing and reproducible results.
Best practices for ML Ops
Principles of ML Ops apply to every stage of a machine learning lifecycle.
Changing anything changes everything (CACE). Every iterative change to a model or data set must be recorded and tested.
Every model, table, and test must be perfectly reproducible given the same conditions and data.
Features and changes must be transparent and shared across participating data teams.
Model versioning and lineage must be meticulously tracked and maintained throughout the ML lifecycle to ensure precise, accurate governance.
HPE and ML Ops
ML Ops has a prominent role in the future of enterprise computing, and HPE is committed to exploring the full potential of machine learning and incorporating ML Ops into our development strategies.
HPE GreenLake for ML Ops clears the way for machine learning projects with an edge-to-cloud platform and consumption-based pricing that allow you advance seamlessly from project planning to production deployments. HPE Apollo hardware and HPE Ezmeral software support every aspect of your ML workload, from data preparation to model building, training, deployment, management, and collaboration.
HPE Ezmeral ML Ops is an end-to-end data science solution with the flexibility to run on-premises, in multiple public clouds, or in a hybrid model and respond to dynamic business requirements in a variety of use cases. HPE Ezmeral ML Ops addresses the challenges of operationalizing ML models at enterprise scale by delivering a cloud-like experience, combined with pre-packaged tools to operationalize the machine learning lifecycle. HPE Ezmeral ML Ops lets customers build, train, and deploy models with DevOps-like speed and agility. It provides a single platform that addresses all aspects of the ML lifecycle—from data prep to model building, training, deployment, monitoring, and collaboration—and operationalizes end-to-end processes across the ML lifecycle, speeding up data model timelines, and reducing time to market.