Productionizing Machine Learning with a Microservices Architecture
Deploying machine learning models from training to production requires companies to deal with the complexity of moving workloads through different pipelines and re-writing code from scratch. Yaron Haviv will explain how to automatically transfer machine learning models to production by running Spark as a microservice for inferencing, achieving auto-scaling, versioning and security. He will demonstrate how to feed feature vectors aggregated from multivariate real-time and historical data to machine learning models and serverless functions for real-time dashboards and actions. About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business. Read more here: https://databricks.com/product/unifie... Connect with us: Website: https://databricks.com Facebook: / databricksinc Twitter: / databricks LinkedIn: / databricks Instagram: / databricksinc Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. https://databricks.com/databricks-nam...

MLflow Pipelines: Accelerating MLOps from Development to Production

Kafka Tutorial for Beginners | Everything you need to get started

Machine Learning Model Deployment: Strategy to Implementation

Running Apache Spark on Kubernetes: Best Practices and Pitfalls

Intro to Databricks Lakehouse Platform Architecture and Security

Build GraphQL Services with Spring Boot like Netflix

Top 5 techniques for building the worst microservice system ever - William Brander - NDC London 2023

Fine Tuning and Enhancing Performance of Apache Spark Jobs

Building a Real-Time Feature Store at iFood

AWS re:Invent 2020: Architectural best practices for machine learning applications

MLOps on Databricks: A How-To Guide

Mastering Chaos - A Netflix Guide to Microservices

Design Microservice Architectures the Right Way

Webinar: MLOps automation with Git Based CI/CD for ML

Don’t Build a Distributed Monolith: How to Avoid Doing Microservices Wrong - Jonathan J. Tower

Deploy Your ML Models to Production at Scale with Amazon SageMaker

Building a Modern Machine Learning Platform on Kubernetes | Lyft

Microservices explained - the What, Why and How?

Data Consistency in Microservices Architecture (Grygoriy Gonchar)

