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ML Model Deployment and Scoring on the Edge with Automatic ML & DF

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Machine Learning Model Deployment and Scoring on the Edge with Automatic Machine Learning and Data Flow

YouTube Video URL: https://youtu.be/gB0bTH-L6DE

Deploying Machine Learning models to the edge can present significant ML/IoT challenges centered around the need for low latency and accurate scoring on minimal resource environments. H2O.ai's Driverless AI AutoML and Cloudera Data Flow work nicely together to solve this challenge. Driverless AI automates the building of accurate Machine Learning models, which are deployed as light footprint and low latency Java or C++ artifacts, also known as a MOJO (Model Optimized). And Cloudera Data Flow leverage Apache NiFi that offers an innovative data flow framework to host MOJOs to make predictions on data moving on the edge.

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ML Model Deployment and Scoring on the Edge with Automatic ML & DF

  1. 1. ML Model Deployment and Scoring on the Edge with Automatic ML & DF James Medel Engineer & Community Maker Greg Keys, PhD Senior Solutions Engineer
  2. 2. Confidential2 Agenda Overview Challenges of ML Model Deployment 02 01 2 demos Model Deployment: H2O on CDF 03 04 ML Model Deployment and Scoring H2O.ai AI Platforms, Cloudera Data Flow 05 Background Demos
  3. 3. Confidential3 A Sample of Machine Learning Use Cases Machine Learning predictive algorithms are beginning to “eat the world” Wholesale / Commercial Banking • Know Your Customers (KYC) • Anti-Money Laundering (AML) Card / Payments Business • Transaction frauds • Collusion fraud • Real-time targeting • Credit risk scoring • In-context promotion Retail Banking • Deposit fraud • Customer churn prediction • Auto-loan Financial Services • Early cancer detection • Product recommendations • Personalized prescription matching • Medical claim fraud detection • Flu season prediction • Drug discovery • ER and hospital management • Remote patient monitoring • Medical test predictions Healthcare and Life Science • Predictive maintenance • Avoidable truck-rolls • Customer churn prediction • Improved customer viewing experience • Master data management • In-context promotions • Intelligent ad placements • Personalized program recommendations Telecom • Funnel predictions • Personalized ads • Credit scoring • Fraud detection • Next best offer • Next best customer • Smart profiling • Prediction • Customer recommendations • Ad predictions and spend Marketing and Retail
  4. 4. Confidential4 Confidential and property of H2O.ai. All rights reserved ML Model Lifecycle (super high level) Data acquisition and prep Model Building Model Deployment Data engineer Data scientist IT / DevOps Business Value predictive analytics actionable responses software applicationspredictive model deploybuildML Algos & techniques significant challenge
  5. 5. Confidential5 Confidential and property of H2O.ai. All rights reserved ML Challenges: Model Building and Deployment Model Building Model Deployment Data scientist IT / DevOps Time Numerous iterations across experiments coding, algos, hyperparams, feature engineering, scoring metric, imbalanced data, etc Talent Skill shortage Trust Is the model biased? Is it overfit? etc Diverse Targets Diverse target environments Java, C++, Python Rest server, Relational DB, Kafka queue, IoT device, batch, streaming, etc Hand-off Data scientist to DevOps: what do I do with this? Does Dev need to write logic or data pipeline? Repeatable? Latency & Throughput How many predictions per second can this make? predictive model
  6. 6. Confidential6 H2O and the ML Challenge Model Building Model Deployment Data scientist IT / DevOps predictive model AutoML Find best model in shortest amount of time while retaining control MOJO generated by AutoML Deployed MOJO MOJO: Let’s drill down! Your infrastucture Your software / integration Compute / AI heuristics / genetic algorithm based Code based against massive datasets (TBs) A different meetup :) MOJO = Flexible, easy to deploy, low-latency scoring software artifact Demo today: Deploy to
  7. 7. Confidential7 MOJO: Highly Flexible Deployment Ready Artifact Flexible - same MOJO deployable to: Infrastructure layer: Cloud, On-Prem, Edge, Device Runtime: Java, C++, Python Data speed: Batch, Realtime/Streaming Target deployment: See list at right for examples Fast: Low Latency Scoring (typically < 1 ms) Familiar Algos: Generalized Linear Model (GLM), Gradient Boosting Machine (GBM), XGBoost, Stacked Ensembles ... MOJO export Java example “Train once, deploy anywhere” Can integrate into SDLC tooling & process
  8. 8. Confidential8 Confidential and property of H2O.ai. All rights reserved Challenges: Deploying Models to the Edge More challenging server edge ● Low compute resources (cpu, mem, storage) ● Minimum higher-level frameworks to tie into (simplicity / barebones) ● Often high throughput data ● Typical need for fast scoring ● High compute resources (cpu, mem, storage) ● Higher-level frameworks to tie into (e.g. web server, spark streaming, UDF) ● Diverse data speeds ● Diverse latency requirements (e.g. low for batch) Train once, deploy anywhere: MOJO created by model building flexibly deploys to full spectrum of targets
  9. 9. Confidential9 Model Training Scoring MOJO Smartphone Device Manufacturing step Engine Predictive Maintenance Direct plugin to Edge & IoT Production Cases Learning Feedback Loop MOJO on the edge for Full ML Lifecycle Load Data Run AutoML Winning Model Generated Model Deployment Scoring history Analytics (Drift detection) Retraining deploy Return data (inputs, prediction, shapley values, etc) MOJO Prediction / response MOJO api data input
  10. 10. Confidential10 Confidential10 • Automatic feature engineering, machine learning and interpretability • Fully automated machine learning from ingest to deployment • User licenses on a per seat basis annually • GUI-based interface for end-to-end data science • A new and innovated platform to make your own AI apps • Enterprise commercial software • Easy and intuitive platform to have AI answer your question H2O.ai: AI Platforms In-memory, distributed machine learning algorithms with H2O Flow GUI Open Source H2O Driverless AI H2O Q • 100% open source – Apache V2 Licensed • Integration with Apache Spark • Enterprise support subscriptions • Interface using R, Python on H2O Flow
  11. 11. Confidential11 Confidential11 Cloudera Data Flow Platform (for Mojo deployment)
  12. 12. Confidential12 Confidential and property of H2O.ai. All rights reserved Model Deployment with H2O + CDF • CDF can execute embedded H2O ML Models to make predictions • CDF can execute H2O ML Models via REST Calls to make predictions • H2O: Driverless AI MOJO Scoring Pipeline, H20-3 MOJO • Cloudera: CDF, NiFi, MiNiFi C++, Kafka, Flink, Spark Streaming • Use Cases: real-time scoring, batch scoring H2O MOJO Inside H2O MOJO Inside H2O MOJO Inside H2O MOJO Inside
  13. 13. Confidential13 Confidential and property of H2O.ai. All rights reserved Model Deployment with Driverless AI + NiFi • Custom NiFi Processor executes Driverless AI Mojo Scoring Pipeline in Java Runtime to make predictions • Capable of doing real-time and batch scoring • Ingest any data source supported by NiFi’s Record Reader • Output any data format supported by NiFi’s Record Writer • Example Use Case: Classify Hydraulic Cooling Condition
  14. 14. Confidential14 Confidential and property of H2O.ai. All rights reserved MOJO deployed to NiFi Demos
  15. 15. Confidential15 Confidential and property of H2O.ai. All rights reserved Model Deployment with Driverless AI + MiNiFi C++ • Custom MiNiFi Processor executes Driverless AI Mojo Scoring Pipeline in Py Runtime to make predictions • Capable of doing real-time and batch scoring • Ingest any data source supported by H2O’s Py DataTable Reader • Output pandas data format • Example Use Case: Classify Hydraulic Cooling Condition
  16. 16. Confidential16 Confidential and property of H2O.ai. All rights reserved Deployment with Driverless AI + Apache Flink • Custom Flink DataStream Job will execute Driverless AI Mojo Scoring Pipeline in Java Runtime to do real-time scoring • Custom Flink DataSet Job will execute Driverless AI Mojo Scoring Pipeline in Java Runtime to do batch scoring • Ingest csv data source • Write predictions to csv • Example Use Case: Classify Hydraulic Cooling Condition
  17. 17. Confidential17 Resources • dai-deployment-examples/ • Github: Apache NiFi • Github: Apache NiFi - MiNiFi C++ • Driverless AI Tutorials • Driverless AI MOJO Docs • Github: H2O-3 • H2O-3 MOJO Docs • YouTube: MiNiFi Custom Processor for Running the MOJO in MiNiFi Data Flow • • • NiFi Contributor Guide • MiNiFi C++ Contributor Guide • Contributing to H2O.ai Tutorials • • Contributing to H2O-3
  18. 18. Confidential18 H2O.ai Learning Center What? • Self paced tutorials • Instructor led courses – AI and ML Foundations (Free) • Knowledge Achievement: Badges H2O.ai Aquarium • Cloud H2O.ai learning environments • Driverless AI, H2O-3, Sparkling Water, DataTable
  19. 19. Questions? https://training.h2o.ai/ai-and-ml-foundations-courses
  20. 20. CONFIDENTIA Thank you

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