Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

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

803 views

Published on

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.

Published in: Software
  • Be the first to comment

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

  1. 1. “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
  2. 2. 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
  3. 3. 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)
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. DEMO https://joinslack.pipeline.ai - join the #demo channel /predict cat vs. dog
  10. 10. 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
  11. 11. 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
  12. 12. Validate new model performance
  13. 13. 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
  14. 14. Slack - Train Model /label https://images.ctfassets.net/kvimhx6nhg7h/5WclEHFxUksuS2IwsUE CE6/a29fa96920666f9d4eb7c456403e4f9d/Tan-cat-in-a-cone.png cat
  15. 15. Slack API: Outbound Webhook to PipelineAI REST API
  16. 16. WORKSHOP https://community.pipeline.ai - Notebooks => 00_Explore_Environment
  17. 17. Thank You! 17 Privacy Accuracy Latency Contact me: chris@pipeline.ai https://community.pipeline.ai

×