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Continuous Delivery of Machine Learning Pipelines

Dr. Jagreet Kaur Gill | 26 Jul 2018

Continuous Delivery for Machine Learning and AI Models

Automate the process of Machine Learning from model building to model deployment in production. Automation involves following operations followed by uniting them -

  • Build a Machine Learning Model
  • Integrate Machine Learning model with the existing Data Pipelines
  • Train model over a large dataset
  • Versioning of Machine Learning/Deep Learning models
  • Alpha/Beta testing of Machine Learning models
  • Analyze the performance of various models
  • Serve Machine Learning models
  • Continuous deployment of Machine Learning models

Challenge for Deploying CI/CD Pipelines for Machine Learning and AI at Scale

  • Develop a platform that can build, version, validate and serve Machine Learning models.
  • Establish a standardized platform that enables cross-company sharing of features, data, and components involving consistent training, streaming, scoring logic.
  • Inconsistency between Machine Learning Workflows.
  • Team Struggle to initiate Machine Learning.
  • Existing Machine Learning workflows are slow, fragmented and brittle.

Solution Offerings for Devops for Machine learning and AI

AKIRA.AI platform to perform Model Building, Validation, Versioning, Serving and Deployment of Machine Learning Models involving following features -

  • Distributed training of Machine Learning models over Big Data
  • Model Versioning
  • Machine Learning Model Analytics
  • Model Validation
  • Model Visualization
  • Model Impact Analysis
  • Model comparison
  • Model Serving in Production and Sandbox environments

Technology Stack -

  • Model building
  • Tensorflow, Keras, Scikit-Learn
  • Model training Distributed / Standalone
  • Data Warehouse
  • Data Pipeline
  • Data Visualization
  • Model Versioning and Serving
  • Deployment

Understanding Machine Learning Framework

Machine Learning Models involve Feature Engineering, Evaluating Model Performance, Scalability, Data Preparation Capabilities, Basic and Advanced Algorithms used for making AI applications.

Lifecycle of deploying Machine Learning Model involves -

  • Define KPIs
  • Retrieving Data
  • Data Preprocessing & Cleansing
  • Data Exploration & Visualization
  • Data Modeling
  • Model Deployment

Build, Train and Deployment of Model Comprises of -

  • Collection and Preparation of Data
  • Algorithm and Framework Selection
  • Tune Model to get Predictions
  • API Building and Integration of Model with Application
  • Deployment of Application on Infrastructure

Real-Time Applications of Machine Learning exists in -

  • Prediction of EarthQuakes and Droughts
  • Object and Face Detection
  • Stock Marketing and Financial Trading
  • Cyber Security and Banking Sectors
  • Healthcare for Diagnosis
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