Get Latest De

Email:info@onlinetrainings.in

Deployment Of Machine Learning Models

Course

DEPLOYMENT OF MACHINE LEARNING MODELS

Category

Machine Learning IT Training

Eligibility

Graduates and Technology Aspirants

Mode

Online and Classroom Sessions

Batches

Week Days and Week Ends

Duration :

Fast Track and Regular 60 Days

Machine Learning Objectives

•How to Properly Install Machine Learning.
•You will learn how to install Machine Learning.
•Learn to code with Machine Learning the easy way.
•A introductory understanding of how to program in Machine Learning.
•Learn and Master Machine Learning with this time saving course
•Learn to design and run complex automated workflows for Machine Learning
•Beginner to Advance Level: Learn to Plan, Design and Implement Machine LearningYou will have a strong understanding about how to develop Machine Learning project.
•Learn Machine Learning from beginner to advanced level. Learn with examples and interactive sessions.

deployment of machine learning models Course Highlights

•Most comprehensive Industrry curriculum
•Basic Training starting with fundamentals
•Real time live project training and Guidance
•We enage Experienced trainers for Quality Training
•Highly Experienced Trainer with 10+ Years in MNC Company
•Project manager can be assigned to track candidates’ performance
•Training time :  Week Day / Week End – Any Day Any Time – Students can come and study
•We help the students in building the resume boost their knowledge by providing useful Interview tips

Who are eligible for Machine Learning

•big data analytics, java, J2ee, Ui Development, user interface designing, Big Data, spark, scala, pyspark, python, cloudera, aws, Industry Marketing, business
•Deep Learning, C, C++, Algorithm, Data Structures, Machine Learning, Artificial Intelligence, Development, C++ Developer, C Programming, Programming, Gpu
•Java Developer, Salesforce Developer, Solution Consulting, Qa Testing, Finance Executive, Full Stack Developer, Email Campaign, React.js, Ui Development
•Oracle Developers, Web Designing, Web Development, Web Technologies, photoshop, illustrator, user interface designing, brochures, Digital Content, ui
•Web Apps, ios/android/windows, Ux Designers, web/mobile developer, html5/css3/javascript/mobile code, testing, automation, manual, mobile, web, ui

DEPLOYMENT OF MACHINE LEARNING MODELS Syllabus

Introduction to the course
•Course curriculum overview
•Knowledge requirements
•Course Pacing and Practice
•Course Tips
•Guidelines on how to approach the course
•Installing Python in your computer
•Slides covered in this course
•Notes covered in this course
•FAQ: Where can I learn more about the required skills?
•Machine Learning Pipeline – Research Environment
•Machine Learning Pipeline: Overview
•Machine Learning Pipeline: Feature Engineering
•Machine Learning Pipeline: Feature Selection
•Machine Learning Pipeline: Model Building
•Jupyter notebooks covered in this section
•Note on library versions – DO NOT SKIP
•Download the data set
•Data Analysis – Demo
•Feature Engineering – Demo
•Feature Selection – Demo
•Model Building – Demo
•Getting Ready for Deployment – Demo
•Bonus: Machine Learning Pipeline: Additional Resources
•Randomness in Machine Learning – Setting the Seed
•FAQ: Where can I learn more about the pipeline steps?
•Develop a Machine Learning Pipeline for Classification
•1 question
•Machine Learning System Architecture
•Machine Learning System Architecture and Why it Matters
•Specific Challenges of Machine Learning Systems
•Machine Learning System Approaches
•Machine Learning System Component Breakdown
•Building a Reproducible Machine Learning Pipeline
•Challenges to Reproducibility – OPTIONAL
•Architecture to Minimise Reproducibility Challenges
•Additional Reading Resources
•Machine Learning Pipeline: Writing Production Code
•Production Code: Overview
•Procedural Programming Pipeline
•Procedural Programing: House Prices Demo
•Assignment: Procedural Programming
•Designing a Custom Pipeline
•Designing a Custom Pipeline Demo Files
•Custom Pipeline Processing steps
•Custom Pipeline Fit and Transform
•Executing the Custom Pipeline
•Leveraging a Third Party Pipeline: Scikit-Learn
•Shallow Dive into Scikit-learn API
•Third Party Pipeline: Demo Files
•Scikit-Learn compatible Transformers
•Executing the Deployment Pipeline
•Third Party Pipeline: Closing Remarks
•Production Code – Third Party Pipeline
•Bonus: Additional Resources on Scikit-Learn
•BONUS: Open Source Libraries for Feature Engineering
•BONUS: Should feature selection be part of the pipeline?
•Bonus: Resources to Improve as a Python Developer
•Course Setup and Key Tools
•Section 5.1 – Introduction
•Section 5.2 – Installing and Configuring Git
•Section 5.3 – How to Use the Course Resources, Monorepos + Git Refresher
•Our Github repository
•Section5.3b – Opening Pull Requests
•Section5.3c – Primer on Monorepos
•Section 5.4a – Operating System Differences and Gotchas
•Section 5.4b – System Path and Pythonpath Demo
•Section 5.5a – Quick Word for More Advanced Students
•Section5.5b – Virtualenv Introduction
•Section5.5c – Requirements files Introduction
•Section5.5d – Virtualenv refresher
•Section 5.6 – Text Editors / IDEs
•Section 5.7 – Engineering and Python Best Practices
•Section 5.8 – Introduction to Pytest
•Section 5.9 – Introduction to Tox [DO NOT SKIP]
•Section 5.10 – Wrap Up
•Creating a Machine Learning Pipeline Application
•6.1 – Introduction
•6.1B – GOTCHA FOR STUDENTS ENROLLED PRIOR TO April 04, 2020
•6.1C – Don’t forget to download the data from Kaggle
•6.2 – Training the Model
•6.3 – Connecting the Pipeline
•6.4 – Making Predictions with the Model
•6.5 – Data Validation in the Model Package
•6.6 – Feature Engineering in the Pipeline
•6.7 – Versioning and Logging
•6.8 – Building the Package
•6.9 – Wrap Up
•Serving the model via REST API
•7.1 – Introduction
•7.2 – Creating the API Skeleton
•7.2b – Flask Crash Course
•7.3 – Adding Config and Logging
•7.4 – Adding the Prediction Endpoint
•7.5 – Adding a Version Endpoint
•7.6 – API Schema Validation
•7.7 – Wrap Up
•Continuous Integration and Deployment Pipelines
•8.1 – Introduction to CI/CD
•8.2 – Setting up CircleCI
•8.3 – Setup Circle CI Config
•8.4a – Gotchas
•8.4 – Publishing the Model to Gemfury
•8.5 – Testing the CI Pipeline
•8.6 – Wrap Up
•Differential Testing
•9.1 – Introduction
•9.2 – Setting up Differential Tests
•9.3 – Differential Tests in CI (Part 1 of 2)
•9.4 – Differential Tests in CI (Part 2 of 2)
•9.5 Wrap Up
•Deploying to a PaaS (Heroku) without Containers
•10.1 – Introduction
•10.2 – Heroku Account Creation
•10.3a – Heroku Gotchas
•10.3 – Heroku Config
•10.4 – Testing the Deployment Manually
•10.5 – Deploying to Heroku via CI
•10.6 – Wrap Up
•Running Apps with Containers (Docker)
•11.1 Introduction to Containers and Docker
•11.2 Installing Docker
•11.3 Creating Our API App Dockerfile
•11.4 Building and Running the Docker Container
•11.5a: Heroku-Docker Gotchas
•11.5 Releasing to Heroku with Docker
•11.6 – Wrap Up
•Deploying to IaaS (AWS ECS)
•12.1 – Introduction to AWS
•12.2 – AWS Costs and Caution
•12.3a – Intro to AWS ECS
•12.3b – Container Orchestration Options: Kubernetes, ECS, Docker Swarm
•12.4 – Create an AWS Account
•12.5 – Setting Permissions with IAM
•12.6 – Installing the AWS CLI
•12.7 – Configuring the AWS CLI
•12.8 – Intro the Elastic Container Registry (ECR)
•12.9 – Uploading Images to the Elastic Container Registry (ECR)
•12.10 – Creating the ECS Cluster with Fargate Launch Method
•12.11 – Creating the ECS Cluster with the EC2 Launch Method
•12.12 – Updating the Cluster Containers
•12.13 – Tearing down the ECS Cluster
•12.14 – Deploying to ECS via the CI pipeline
•12.15 – Wrap Up
•A Deep Learning Model with Big Data
•Challenges of using Big Data in Machine Learning
•Installing Keras
•Introduction to a Large Dataset – Plant Seedlings Images
•Building a CNN in the Research Environment
•Production Code for a CNN Learning Pipeline
•Reproducibility in Neural Networks
•Setting the Seed for Keras
•Seed for Neural Networks – Additional reading resources
•13.8 – Packaging the CNN
•13.9 – Adding the CNN to the API
•13.10 – Additional Considerations and Wrap Up
•Common Issues found during deployment
•Troubleshooting
•Final Section
•BONUS LECTURE – THERE IS MORE…