Realize the Benefits of AI with the Right Framework
In the current economic environment, organizations are focused on KPIs and metrics that matter today and in the near term. They are adopting analytics in the cloud, many by building cloud lakehouses, which blend the best of both worlds in cloud data warehouses and data lakes, enabling them to handle any type of data (structured, semi-structured, and unstructured), coming at any speed (batch and real-time), and in petabyte-scale volumes. Analytics modernization in the cloud offers potentially big benefits: cost savings, supply chain optimization, increased productivity, improved operational efficiency, and more. Similar potential benefits surround more advanced predictive analytics, AI, and ML projects. But AI/ML is hard and new to many organizations. In these times, project success is paramount, and it requires the right framework.
According to Gartner, “A litmus test of organizations’ maturity is how quickly and repeatedly they can get these AI systems into production. Our surveys are showing that organizations are not managing to do this as quickly as they had hoped. The result is an organizational schism, given the high expectations executive boards have regarding the transformative power of AI.”[1] In effect, AI benefits can only be realized when you can productize your machine learning models successfully, on time, and at scale. Hence, the need for machine learning operations (MLOps).
"MLOps is the process of operationalizing your machine learning models."
But it needs a framework. When AI/ML projects lack a framework and architecture to support model building, deployment, and monitoring – they fail. To succeed, you need collaboration between data scientists and data engineers for automating and productizing machine-learning algorithms.
DataOps provides a way to operationalize your data platform by extending the concepts of DevOps to the world of data. Extending DevOps, DataOps is built on a simple framework of CI/CD: continuous integration, continuous delivery, and continuous deployment. When you extend this framework further with on an onramp of a data marketplace, you get a solid framework that is MLOps.
There are five steps that form the framework for successful MLOps.
A systematic approach as laid out in the MLOps framework above is essential to the success of your data science use cases. The Informatica Data Engineering product portfolio provides end-to-end functionality for MLOps.
Join us at the upcoming webinar MLOps: 5 Steps to Operationalize Data Science to learn more from the experts in MLOps. Additionally, download the whitepaper with a blueprint for MLOps.
[1] Gartner, “Predicts 2020: Artificial Intelligence — the Road to Production,” by Anthony Mullen, Saniye Alaybeyi, Van Baker, Arun Chandrasekaran, Alexander Linden, Magnus Revang, Svetlana Sicular, 2 December 2019
Jun 22, 2022
Jun 22, 2022