Inference at Scale with Apache Beam
Learn to deploy and scale machine learning models efficiently using Apache Beam for distributed inference on CPUs and GPUs, with practical insights on parallelizing workloads.
Batch and Stream Analysis with TypeScript Using Apache Beam
Explore batch and stream analysis using Beam's Typescript SDK. Learn API choices, usability, and functionality through demos. Discover how to leverage Typescript for efficient data processing workflows.
Machine Learning with Apache Beam
Explore Apache Beam for distributed data pipelines in machine learning, covering inference, data processing, and training with hands-on demos for parallelizing ML workloads.
Enhancing Flexibility and Productivity with Access Patterns and Storage-Agnostic Abstractions
Explore a platform bridging data management gaps, offering storage-agnostic abstractions and ACID guarantees across streaming, batch, and random-access storages for flexible, consistent data handling.
Improve Your App UX with One Model Processing Insights from Batch and Streams
Learn to enhance app UX using a unified model for batch and stream processing. Explore Dataflow's capabilities through a real-time demo, gaining practical insights on managing and visualizing data streams.
From Experimentation to Products - The Production Machine Learning Journey
Explore the journey from ML experimentation to production, covering MLOps, TensorFlow Extended, and pipeline architectures for scalable, maintainable machine learning applications.
Apache Beam - Making Big Data Portable With gRPC
Explore Apache Beam's unified programming model for efficient, portable data processing pipelines. Learn about its architecture, runners, and gRPC-based portability framework for cross-language execution.
How Apache Beam Makes Your Data Processing Go
Explore Apache Beam's Go SDK for efficient distributed data processing, overcoming Go's unique challenges using reflection, interfaces, and generics for scalable pipeline execution.
Introduction to Apache Beam with Examples
Learn Apache Beam's unified model for batch and streaming data processing, with real-world examples from eBay and Mozilla's data pipelines.
Google Dataflow - The New Open Model for Batch and Stream Processing
Explore Google Dataflow's unified batch and stream processing model through real-time demonstrations, practical insights, and visualization techniques for managing data streams.
Video Analytics for Football Games
Explore real-time video analytics for football games using Apache Beam, machine learning, and cloud technologies to detect events, track players and ball, and perform game analytics.
Using Open Source Tech to Swap Out Components of Your Data Pipeline
Explore open-source technologies for flexible data pipelines, from Apache Kafka to BigQuery, with easy component swapping and cloud deployment options.
Building ML Workflows with Java, Python & Apache Beam
Learn to create multi-language ML pipelines using Apache Beam, combining Java for data processing and Python for ML modeling in batch and streaming workflows.
Whisky Clustering with Apache Projects - Groovy, Commons Math, Ignite, Spark, Wayang, Beam and Flink
Analyze top distilleries' whiskeys using K-means clustering from Apache projects to identify the perfect single-malt Scotch. Explore data analysis with Groovy, Commons Math, Ignite, Spark, Wayang, Beam, and Flink.
Bringing Machine Learning to Streaming Data with a Few Lines of Code
Explore ML-powered stream processing with Dataflow and Apache Beam. Learn about Dataflow ML's integration with Vertex AI and Etsy's customer analytics platform journey.
Reinvent Yourself and Be Curious - MLOps Career Insights - Podcast #264
Explore MLOps career transitions, essential pipeline components, and practical challenges with insights from an experienced engineer. Gain valuable perspectives on industry trends and best practices.
Real-Time Machine Learning: Features and Inference - MLOps Podcast #135
Explore real-time ML features and inference with LinkedIn experts. Gain insights on implementation, challenges, and best practices for moving from batch to interactive near real-time machine learning.