Next Meetup

Continuous ML in Prod with PipelineAI, TensorFlow, Kafka + GPU + Stateful K8s
Aligned with the O'Reilly AI London Conference Oct 8-11 MEETUP IS OPEN TO THE PUBLIC. YOU DO NOT NEED A CONFERENCE TICKET. ********************** Updated Sept 15, 2018: 2 FREE TICKETS TO O'Reilly AI Conference! In order to qualify, please navigate to https://community.pipeline.ai and deploy one of our sample models using TensorFlow, Keras, Scikit-Learn, MXNet, Python, Spark, XGBoost, and all major ML/AI Frameworks. Email Us ([masked]) the Link to the Live Model in Production - and You'll be Eligible to Receive 1 of 2 Free Tickets to the AI Conference in London! ********************** Talk 0: Meetup News and Updates Talk 1: End-to-End, Multi-Cloud, Continuous Machine Learning in Production with PipelineAI, TensorFlow, and Kafka (Chris Fregly, Founder @ PipelineAI) Abstract https://community.pipeline.ai <== ALL DEMOS AND CODE LIVE HERE Traditional machine learning pipelines end with life-less models sitting on disk in the research lab. These traditional models are typically trained on stale, offline, historical batch data. Static models and stale data are not sufficient to power today's modern, AI-first Enterprises that require continuous model training, continuous model optimizations, and lightning-fast model experiments directly in production. Through a series of open source, hands-on demos and exercises, we will use PipelineAI to breathe life into these models using 4 new techniques that we’ve pioneered: * Continuous Validation (V) * Continuous Optimizing (O) * Continuous Training (T) * Continuous Explainability (E) The Continuous "VOTE" techniques has proven to maximize pipeline efficiency, minimize pipeline costs, and increase pipeline insight at every stage from continuous model training (offline) to live model serving (online.) Bio Chris Fregly is Founder at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs." Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco. Talk 2: GPUs and Stateful Kubernetes (Antje Barth, Partner Engineer @ MapR, Co-Organizer of the EMEA Women in Big Data Meetup) In this talk, you will learn how to build a reliable, scalable, and secure containerized platform, and to handle persistent data in large containerized environments across multiple data centers or geographic locations.

Hilton Metropole, Windsor Suite

225 Edgware Road · London

What we're about

Spark and Deep Learning Experts digging deep into the internals of Spark Core, Spark SQL, DataFrames, Spark Streaming, MLlib, Graph X, BlinkDB, TensorFlow, Caffe, Theano, OpenDeep, DeepLearning4J, etc.

Deeper than a blog post or typical meetup, we'll explore and discuss the best practices and idioms of the code base across many areas including Spark's JVM Bytecode Generation, CPU-cache-aware Data Structures and Algorithms, Approximations, Probabilistic Data Structures, Shuffle and I/O Optimizations, Streaming Micro-batch Scheduling, Performance Tuning, Configuration, Monitoring, Auto-Scaling, etc.

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