Machine Learning (ML) has empowered a broad range of industries including life sciences, manufacturing, Consumer goods, financial services, and telecom. Despite its ubiquity, many enterprises face myriad challenges and shortcomings in developing, deploying and managing their Machine Learning applications and find it difficult to shift from experimentation to production grade AI. The need to adopt DevOps practices in ML to increase automation whilst addressing business and regulatory requirements is becoming a foundational need that’s converging into a set of common practices, tools and governance functions under Machine Learning Operations or MLOps.
MLOps encompasses combination of practices and processes that aim to make seamless and efficient development, deployment, scaling and maintenance of Machine Learning models. HCL's MLOps framework is a cloud native approach to productionizing AI experiments to enterprise scale. Built from the experience of dozens of experiments and production deployments, HCL framework fuses the growing capabilities of cloud hyperscale, specialized AI tools and tribal knowledge gained within the enterprise across its AI maturity curve. Download the brochure to know more.