Abstract: Most data science teams start their AI journey from what they perceive to be the logical beginning: building AI models using manually extracted datasets. Operationalizing machine learning, in the sense of considering all the requirements of the business; handling online and federated data sources, scale, performance, security, continuous operations, etc. comes as an afterthought, making it hard and resource-intensive to create real business value with AI.
Today, forward-thinking enterprises are taking a new, production-first approach to MLOps. This means designing a continuous operational pipeline, and then making sure the various components and practices map into it. Automating as many components as possible, constantly measuring business metrics and making the process repeatable, so that it generates measurable ROI for the business.
In this session, we will describe the challenges in operationalizing machine & deep learning. We’ll explain the production-first approach to MLOps pipelines - using a modular strategy, where the different components provide a continuous, automated, and far simpler way to move from research and development to scalable production pipelines. Without the need to refactor code, add glue logic, and spend significant efforts on data and ML engineering.
We will cover various real-world implementations and examples, and discuss the different stages, including automating feature creation using a feature store, building CI/CD automation for models and apps, deploying real-time application pipelines, observing the model and application results, creating a feedback loop and re-training with fresh data.
Bio: Yaron Haviv is a serial entrepreneur who has been applying his deep technological experience in data, cloud, AI and networking to leading startups and enterprise companies since the late 1990s. As the co-founder and CTO of Iguazio, Yaron drives the strategy for the company’s data science platform and leads the shift towards real-time AI. He also initiated and built Nuclio, a leading open source serverless platform with over 3,400 Github stars and MLRun, Iguazio’s open source MLOps orchestration framework.