PipelineAI Continuous Machine Learning and AI - Rework Deep Learning Summit - San Francisco, CA - Jan 25, 2019 -

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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.) Attendees will learn to create continuous machine learning pipelines in production with PipelineAI, TensorFlow, and Kafka.

Software

“Halliburton chooses PipelineAI to power its Oil & Gas Vertical Cloud”
(LIFE Conference Keynote 2018)
“PipelineAI is…
Uber Michelangelo for
AI-First Enterprises.”
“PipelineAI is…
AWS SageMaker for
Industry Vertical
Clouds.”
Chris Fregly
Founder @ PipelineAI
chris@pipeline.ai
Deep Learning Summit
San Francisco, CA
Jan 25, 2019

Problem 2
It’s Hard to Balance the 3 “Cy’s” of AI
Privacy
Accuracy Latency
Solution: Experiment in Live Production to Find the Right Balance

Current Solution: Cloud Lock-In 3
https://aws.amazon.com/blogs/machine-learning/automated-and-continuous-deployment-of-amazon-sagemaker-models-with-aws-step-functions/ (Dec 2018)

PipelineAI Solution: 1-Click & Multi-Cloud
x11Generated Models1Original Model x3Clouds
4
Arbitrage cost savings
across
all public cloud providers
Find best performing model
among all generated models

Mission & Value Proposition
5x smaller and 3x faster models
Easy integration with Enterprise systems
Auto-tune accuracy vs. latency vs. privacy vs. cost
Safely explore new models in seconds vs. months
Unified runtime across language, framework & cloud
5
The Premium Enterprise AI Runtime

Perform Online Predictions using Slack
A/B and multi-armed bandit model compare
Train Online Models with Kafka Streams
Create new models quickly
Deploy to production safely
Mirror traffic to validate online performance
PipelineAI: Real-Time Machine Learning

Advantages of PipelineAI
Any Framework, Any Hardware, Any Cloud
Dashboard to manage the lifecycle of models
from local development to live production
Generates optimized runtimes for the models
Custom targeting rules, shadow mode, and
percentage-based rollouts to safely test features
in live production
Continuous model training, model validation, and
pipeline optimization

Market Validation 8
Existing AI Industry Vertical Clouds
GE Edison
Salesforce Einstein
PipelineAI-based Vertical Clouds
Halliburton Open Earth Cloud
Huawei Cloud
Large Travel Enterprise
Large Electronics Manufacturer
Consumer Product Group (CPG) Analytics

DEMO
https://joinslack.pipeline.ai - join the #demo channel
/predict cat vs.
dog

Slack - Predict with Image
Cat?
Dog?
/predict
https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUE
CE6/a29fa96920666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png
Model Variant
Confidence of Each Prediction
Possible Predictions
REQUEST
RESPONSE

COMPOSE/
ENSEMBLE
Architecture for Online Prediction
/predict <img>
Archive
Model 3
(Canary)
Model 1
Model 2
INPUT
ARCHIVE
RESPONSE
REQUEST
Select prediction with highest
confidence (via customizable
Objective Function)
Replay for future use
Compare Canary to live
Model 1 and Model 2
Mirrored Traffic
Live Traffic
Traffic
Routing
/predict: Pass an image URL to classify (cat or dog) via model prediction REST API
/predict_archive

Online Model Training with Streams
/label <img> <label>
Training Stream
Distributed
Filesystem
Deploy model
Model 3
(Canary)
Train model
Model 1
Model 2
/label: Add new training data (human feedback loop) to improve the model
/train: Create a new model with the latest training data
/deploy: Deploy the model as a Canary alongside live models
/route: Mirror the live traffic to Canary to validate model performance
/label_data

Slack - Train Model
/label
https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUE
CE6/a29fa96920666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png
cat

Slack API: Outbound Webhook to PipelineAI REST API

WORKSHOP
https://community.pipeline.ai - Notebooks => 00_Explore_Environment

Thank You! 17
Privacy
Accuracy Latency
Contact me:
chris@pipeline.ai
https://community.pipeline.ai

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Chris Fregly, Founder @ PipelineAI, will walk you through a real-world, complete end-to-end Pipeline-optimization example. We highlight hyper-parameters - and model pipeline phases - that have never been exposed until now. While most Hyperparameter Optimizers stop at the training phase (ie. learning rate, tree depth, ec2 instance type, etc), we extend model validation and tuning into a new post-training optimization phase including 8-bit reduced precision weight quantization and neural network layer fusing - among many other framework and hardware-specific optimizations. Next, we introduce hyperparameters at the prediction phase including request-batch sizing and chipset (CPU v. GPU v. TPU). Lastly, we determine a PipelineAI Efficiency Score of our overall Pipeline including Cost, Accuracy, and Time. We show techniques to maximize this PipelineAI Efficiency Score using our massive PipelineDB along with the Pipeline-wide hyper-parameter tuning techniques mentioned in this talk. Bio Chris Fregly is Founder and Applied AI Engineer 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.

Hyper-Parameter Tuning Across the Entire AI Pipeline GPU Tech Conference San ...

Chris Fregly

PipelineAI Optimizes Your Enterprise AI Pipeline from Distributed Training to...

Chris Fregly

Advanced Spark and TensorFlow Meetup - Dec 12 2017 - Dong Meng, MapR + Kubern...

Chris Fregly

Online Workshop Note: A GPU-based cloud instance will be provided to each attendee for the duration of this event!! At 8am PT on the morning of this workshop, we will email the Webinar details to your email address registered with Eventbrite. If this email address is not up to date - or you do not get the email by 8am PT - please email your Eventbrite confirmation to help@pipeline.ai and we'll send you the details. http://pipeline.ai Title PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop Time Start: 9am PT Time End: 1pm PT Time Highlights We will each build an end-to-end, continuous Tensorflow AI model training and deployment pipeline on our own GPU-based cloud instance. At the end, we will combine our cloud instances to create the LARGEST Distributed Tensorflow AI Training and Serving Cluster in the WORLD! Pre-requisites Just a modern browser, internet connection, and a good night's sleep! We'll provide the rest. Agenda Spark ML TensorFlow AI Storing and Serving Models with HDFS Trade-offs of CPU vs. *GPU, Scale Up vs. Scale Out CUDA + cuDNN GPU Development Overview TensorFlow Model Checkpointing, Saving, Exporting, and Importing Distributed TensorFlow AI Model Training (Distributed Tensorflow) TensorFlow's Accelerated Linear Algebra Framework (XLA) TensorFlow's Just-in-Time (JIT) Compiler, Ahead of Time (AOT) Compiler Centralized Logging and Visualizing of Distributed TensorFlow Training (Tensorboard) Distributed Tensorflow AI Model Serving/Predicting (TensorFlow Serving) Centralized Logging and Metrics Collection (Prometheus, Grafana) Continuous TensorFlow AI Model Deployment (TensorFlow, Airflow) Hybrid Cross-Cloud and On-Premise Deployments (Kubernetes) High-Performance and Fault-Tolerant Micro-services (NetflixOSS) More Info including GitHub and Docker Repos http://pipeline.ai

High Performance Distributed TensorFlow in Production with GPUs - NIPS 2017 -...

Chris Fregly

PipelineAI + TensorFlow AI + Spark ML + Kuberenetes + Istio + AWS SageMaker +...

Chris Fregly

http://pipeline.ai Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models - and the TensorFlow Runtime - in GPU-based production environment. This talk is 100% demo based on open source tools and completely reproducible through Docker on your own GPU cluster. Bio Chris Fregly is Founder and Research Engineer at PipelineAI, a Streaming 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." 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. http://pipeline.ai

Optimizing, Profiling, and Deploying TensorFlow AI Models with GPUs - San Fra...

Chris Fregly

Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool , I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment. This talk contains many demos based on open source tools. You can completely reproduce all demos through Docker on your own GPU cluster. See http://pipeline.ai for links to the GitHub Repo.

Nvidia GPU Tech Conference - Optimizing, Profiling, and Deploying TensorFlow...

Chris Fregly

http://pipeline.ai Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements from Kubernetes, Istio, and TensorFlow. In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment. This is the holy grail of data science - rapid and safe experiments of ML / AI models directly in production. Following the Successful Netflix Culture that I lived and breathed (https://www.slideshare.net/reed2001/culture-1798664/2-Netflix_CultureFreedom_Responsibility2), I give Data Scientists the Freedom and Responsibility to extend their ML / AI pipelines and experiments safely into production. Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong. Learn to be fast and strong by attending this talk. http://pipeline.ai

Building Google Cloud ML Engine From Scratch on AWS with PipelineAI - ODSC Lo...

Chris Fregly

Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT AOT Compiler, and Graph Transform Tool , I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment. This talk is 100% demo based with open source tools and completely reproducible through Docker on your own GPU cluster. In addition, I spin up a GPU cloud instance for every attendee in the audience. We go through the notebooks together as I demonstrate the process of continuously training, optimizing, deploying, and serving a TensorFlow model on a large, distributed cluster of Nvidia GPUs managed by the attendees. http://pipeline.ai

Optimizing, Profiling, and Deploying TensorFlow AI Models in Production with ...

Chris Fregly

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