Building Real-Time ML Pipelines with a Feature Store

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Topic: Building Real-Time ML Pipelines with a Feature Store

Speaker: Gilad Shaham, Director of Product Management, Iguazio

Gilad has over 15 years of experience in product management and a solid R&D background. He combines analytical skills and technical innovation with Data Science market experience. Gilad’s passion is to define a product vision and turn it into reality. As Director of Product Management at Iguazio, Gilad manages both the Enterprise MLOps Platform product as well as MLRun, Iguazio’s open source MLOps orchestration framework. Prior to joining Iguazio, Gilad managed several different products at NICE-Actimize,a leading vendor of financial crime prevention solutions, including coverage of machine-learning based solutions, formation of a marketplace and addressing customer needs across different domains. Gilad holds a B.A in Computer Science, M.Sc. in Biomedical Engineering and MBA from Tel-Aviv University

Abstract:
There are many challenges to operationalizing machine learning, but perhaps one of the most difficult is online feature engineering. Generating a new feature based on batch processing takes an enormous amount of work for ML teams, and those features must be used for the training stage as well as the inference layer. Feature engineering for real-time use cases is even more complex. Real-time pipelines require an extremely fast and low latency event-processing mechanism, that can run complex algorithms to calculate features in real time. With the growing business demand for real-time use cases such as fraud prediction, predictive maintenance and real-time recommendations, ML teams are feeling immense pressure to solve the operational challenges of real-time feature engineering for machine learning, in a simple and reproducible way. This is where online feature stores come in. An online feature store accelerates the development and deployment of real-time AI applications by automating feature engineering and providing a single pane of glass to build, share and manage features across the organization. This improves model accuracy, even when complex calculations and data transformation is involved, saving your team valuable time and providing seamless integration with training, serving and monitoring frameworks.

In this talk, we’ll cover the challenges associated with online feature engineering across training and serving environments, how feature stores enable teams to collaborate on building, sharing and managing features across the organization, solutions that exist to enable you to build a real-time operational ML pipeline that can handle events arriving in ultra-high velocity and high volume, calculate and trigger an action in seconds, how to build your ML pipeline in a way that enables ingestion and analysis of real-time data on the fly and how to monitor your real-time AI applications in production to detect and mitigate drift, to make your method repeatable and resilient to changes in market conditions.

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