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Testing And Monitoring Machine Learning Model Deployments

Course

TESTING AND MONITORING MACHINE LEARNING MODEL DEPLOYMENTS

Category

Machine Learning Tech Training

Eligibility

Lateral Entry Professionals and Freshers

Mode

Regular Offline and Online Live Training

Batches

Week Days and Week Ends

Duration :

45 Days

Machine Learning Objectives

•Best practices for Machine Learning
•What are the advantages of Machine Learning?
•Learn from simple and interactive sessions on Machine Learning
•Learn a few useful and important topics in Machine Learning.
•Learn The Basics of Machine Learning In a Single Course
•Learn the Machine Learning fundamentals you’ll be using day-in and day-outStudents will learn the core concept of making Real Life Project
•Build a strong knowledge base on Machine Learning from Scratch to Advanced level
•Learn Machine Learning from basic to advanced with examples and interactive sessions at peak.

testing and monitoring machine learning model deployments Training Highlights

•Career guidance providing by It Expert
•Get Training from Certified Professionals
• Helps you stand out in a competitive market
•Personal attention and guidance for every student
•Indutry oriented training with corporate casestudies
•Courseware includes reference material to maximize learning.
•Our trainers have experience in training End Users & Students & Corporate employees.
• Our dedicated HR department will help you search jobs as per your module & skill set, thus, drastically reducing the job search time

Who are eligible for Machine Learning

•Architect, Lead, Developer, Project Manager, Verification Engineer, Rtl Design, Physical Design, L3 Support Engineer, Cloud Computing, Big Data Engineer
•Java Developer, Php, Sales Management, Product Management, Software Services, Ui Development, MySQL, MongoDB, Nginx, NoSQL, Solr, Elastic Search, ApacheJava, Net, C#, Manual Testing, Automation Testing, Manual Testing With Healthcare, Android And Ios Developer
•Oracle Apps Testing, Functional Testing, O2C, Techical Support, Service Desk, IT Helpdesk, IT Support, Tech Support, java, J2ee, Java Developer
•Sharepoint Architect, Mobile Architect, MSBI Module Lead, Filenet Developer, WBM, IBM BPM

TESTING AND MONITORING MACHINE LEARNING MODEL DEPLOYMENTS Syllabus

Introduction
•Course Curriculum Overview
•Course Requirements
•How to Approach This Course (Important)
•All Notes & Slides For This Course
•FAQ: I would like to learn more about the topics not covered
•Setting the Scene & ML System Lifecycle
•Deploying a Model to Production
•Course Scenario: Predicting House Sale Price
•Setup A: Python Installation (Important)
•Setup B: Git and Github Setup (Advanced users can skip)
•Course Github Repo & Data
•Download dataset and Github repo: links and guidelines
•Setup C: Jupyter Notebook Setup
•Setup D: Install Notebook Dependencies
•Introduction to the Dataset & Model Pipeline
•ML System Lifecycle
•Additional Links and Resources
•Testing Concepts for ML Systems
•Section Overview
•Testing Focus in This Course
•Why Test?
•The Value of Testing
•Testing Theory
•Testing Machine Learning Systems (Important)
•Setup A: Install Requirements
•Hands-on Assignment: Unit Testing Input Data
•Hands-on Assignment: Unit Testing Data Engineering Code
•Assignment 3: Hands-on Assignment: Unit Testing Model Quality
•Assignment 4: Hands-on Assignment: Unit Testing Model Config
•Wrap Up
•Unit Testing a Production ML Model
•Code Conventions
•Pytest
•Setup – Kaggle Data
•Download the data set – Text Summary
•Setup 2 – Tox
•Code Base Overview
•Preprocessing & Feature Engineering Unit Testing Theory – Why Do This?
•Preprocessing & Feature Engineering Unit Testing
•Quick note on git hygiene for the course
•Model Config Unit Testing Theory – Why Do This?
•Model Config Unit Testing
•Input Data Testing Theory – Why Do This?
•Input Data Unit Testing
•Model Quality Unit Testing Theory – Why Do This?
•Model Quality Unit Testing
•Quick Lecture on Tooling Improvements
•Docker & Docker Compose
•Quick Docker Recap
•Why Use Docker?
•Introduction to Docker Compose
•Docker & Docker Compose Installation
•Windows Specific Docker Issue
•Hands on Exercise: Basic Docker Compose
•Docker Space Consumption Tips
•Integration Testing the ML API
•API Conceptual Guide
•Overview of the Codebase
•Using our Open API Spec Part 1
•WINDOWS SPECIFIC SETUP
•Using our Open API Spec Part 2
•Integration Testing Theory
•WORK AROUND LECTURE – 32 bit Operating Systems
•Integration Testing Hands-On Code
•A note on benchmark integration tests
•Differential Testing
•Differential Testing Theory
•Differential Testing Implementation
•Shadow Mode Deployments
•Shadow Mode Theory
•Testing Models in Production
•Tests in Shadow Deployments
•Code Overview – DB Setup
•WINDOWS port mapping
•Setup Tests for Shadow Mode
•Shadow Mode – Asynchronous Implementation
•Populate Database with Shadow Predictions
•Jupyter Demo – Setup
•Jupyter Demo – Tests in Shadow Mode
•Monitoring – Metrics with Prometheus
•Why Monitor?
•Monitoring Theory
•Metrics for Machine Learning Systems
•Prometheus & Grafana Overview
•[WINDOWS ONLY] Additional Setup
•Basic Prometheus Setup – Hands-on
•Adding Metrics – Hands-on
•Adding Grafana – Hands-on
•Infrastructure Metrics – Hands-on
•Adding Metrics Monitoring to Our Example Project
•Creating an ML System Grafana Dashboard
•Monitoring – Logs with Kibana
•Monitoring Logs for ML – Theory
•The Elastic Stack (Formerly ELK) – Overview
•Kibana Hands-on Exercise
•Integrating Kibana into The Example Project
•Setting Up a Kibana Dashboard for Model Inputs
•Conclusion
•Final Section
•Bonus Lecture – There is more…