MLOps with AWS Bootcamp Zero to Hero Series

MLOps with AWS – Bootcamp – Zero to Hero Series, available at $84.99, has an average rating of 4.56, with 167 lectures, 4 quizzes, based on 629 reviews, and has 6718 subscribers.

You will learn about Configuring the CI/CD Pipeline for Machine Learning Projects Ability to track the source code & training images, configuration files with Git Based Repository – AWS CodeCommit Ability to Perform the Build using AWS CodeBuild Ability to Deploy the Application on Server using AWS CodeDeploy Orchestrate the MLOps steps using AWS CodePipeline Identify appropriate AWS services to implement ML solutions Perform the Load testing Monitoring the End Point Performance Monitoring the Model Drift The ability to follow model-training best practices The ability to follow deployment best practices The ability to follow operational best practices This course is ideal for individuals who are Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews or Anyone interested in learning how Machine Learning is implemented on Large scale data or Anyone interested in AWS cloud-based machine learning and data science or Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud or Anyone looking to learn the best practices to Operationalize the Machine Learning Models It is particularly useful for Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews or Anyone interested in learning how Machine Learning is implemented on Large scale data or Anyone interested in AWS cloud-based machine learning and data science or Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud or Anyone looking to learn the best practices to Operationalize the Machine Learning Models.

Enroll now: MLOps with AWS – Bootcamp – Zero to Hero Series

Summary

Title: MLOps with AWS – Bootcamp – Zero to Hero Series

Price: $84.99

Average Rating: 4.56

Number of Lectures: 167

Number of Quizzes: 4

Number of Published Lectures: 167

Number of Published Quizzes: 4

Number of Curriculum Items: 172

Number of Published Curriculum Objects: 172

Original Price: $189.99

Quality Status: approved

Status: Live

What You Will Learn

Configuring the CI/CD Pipeline for Machine Learning Projects

Ability to track the source code & training images, configuration files with Git Based Repository – AWS CodeCommit

Ability to Perform the Build using AWS CodeBuild

Ability to Deploy the Application on Server using AWS CodeDeploy

Orchestrate the MLOps steps using AWS CodePipeline

Identify appropriate AWS services to implement ML solutions

Perform the Load testing

Monitoring the End Point Performance

Monitoring the Model Drift

The ability to follow model-training best practices

The ability to follow deployment best practices

The ability to follow operational best practices

Who Should Attend

Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews

Anyone interested in learning how Machine Learning is implemented on Large scale data

Anyone interested in AWS cloud-based machine learning and data science

Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud

Anyone looking to learn the best practices to Operationalize the Machine Learning Models

Target Audiences

Anyone preparing for Data Science , Machine Learning & Deep Learning Interviews

Anyone interested in learning how Machine Learning is implemented on Large scale data

Anyone interested in AWS cloud-based machine learning and data science

Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud

Anyone looking to learn the best practices to Operationalize the Machine Learning Models

Welcome to “Practical MLOps for Data Scientists & DevOps Engineers with AWS”

Are you ready to propel your career in artificial intelligence and machine learning (AI/ML) development or data science to new heights? This comprehensive course is meticulously crafted for individuals with aspirations to excel in these domains, providing a Production Level mindset that goes beyond the basics.

Course Overview: Mastering MLOps with AWS

**1. Elevate Your Skills:

Design, build, deploy, optimize, train, tune, and maintain ML solutions using AWS Cloud.

Adopt a Production Level mindset tailored for Machine Learning in conjunction with DevOps best practices.

**2. Beyond Basics:

Employ model-training best practices on extensive cloud-based datasets.

Demonstrate expertise in deployment best practices for consistent functionality.

Implement operational best practices to guarantee zero downtime.

**3. Structured Learning Path:

Follow a logical, structured path with in-depth explanations, practical exercises, and relevant demonstrations.

Gain proficiency in tackling real-world business challenges by implementing scalable solutions on AWS.

Course Structure: Journey Through Mastery

Section 1: Introduction to the AWSMLOPS Course and Instructor

Get acquainted with the course objectives and the experienced instructor leading the way.

Section 2: Understanding MLOps

Delve into the core concepts of MLOps, understanding its significance and application.

Section 3: DevOps Principles for Data Scientists

Explore the principles of DevOps tailored for data scientists, bridging the gap between development and operations.

Section 4: Getting Started with AWS

Acquaint yourself with the AWS platform, laying the foundation for subsequent sections.

Sections 5-16: In-Depth Exploration

A comprehensive exploration of key topics, including AWS CodeBuild, AWS CodeDeploy, AWS CodePipeline, Docker Containers, Amazon SageMaker, Feature Engineering, SageMaker Pipelines, and much more.

Hands-On Learning: Real-World Applications

Tools and Technologies Covered:

Data Ingestion and Collection

Data Processing and ETL (Extract, Transform, Load)

Data Analysis and Visualization

Model Training and Deployment/Inference

Operational Aspects of Machine Learning

AWS Machine Learning Application Services

Notebooks and Integrated Development Environments (IDEs)

Version Control with AWS CodeCommit

Amazon Athena, AWS Batch, Amazon EC2

Amazon Elastic Container Registry (Amazon ECR), AWS Glue

Amazon CloudWatch, AWS Lambda

Amazon S3 for Storage and Scalability

Access to Course Materials:

All course materials, including source code, are available on GitHub for convenient access from anywhere.

Stay updated with the latest advancements through easy access to the latest updates.

Embark on the MLOps Journey: Elevate Your Skills Today

Why Choose This Course?

Gain a Production Level mindset tailored for AI/ML in conjunction with DevOps practices.

Acquire proficiency in deploying solutions on scalable datasets beyond personal laptops.

Comprehensive exploration of AWS services crucial for MLOps.

Real-world applications and hands-on projects for practical learning.

Your Success in MLOps Begins Here:

Equip yourself with the latest tools and best practices on the AWS platform.

Tackle complex business challenges with confidence.

Propel your career to new heights in the world of MLOps.

Enroll Now: Take the leap into mastering MLOps with AWS. Click the “Enroll Now” button to embark on a transformative learning journey. Elevate your AI/ML and DevOps skills to the next level and solve complex business challenges effectively. Your success in the world of MLOps begins here and now!

Course Curriculum

Chapter 1: About AWS MLOps Course and Instructor

Lecture 1: About the MLOps with AWS Course

Lecture 2: How to make the most of this course?

Lecture 3: Source Code of this course

Lecture 4: Slide Resources

Chapter 2: Introduction to MLOps

Lecture 1: What & Why MLOps

Lecture 2: Quick Hands On Demo on MLOps

Lecture 3: MLOps Fundamentals

Lecture 4: MLOps Fundamentals – Deep Dive

Lecture 5: Why DevOps alone is not Suitable for Machine Learning ?

Lecture 6: What is AWS & its Benefits

Lecture 7: Technical Stack of AWS for MLOps & Machine Learning

Chapter 3: DevOps for Data Scientists

Lecture 1: What is SDLC & Why its Important

Lecture 2: Types of SDLC

Lecture 3: Waterfall Vs Agile Vs DevOps

Lecture 4: DevOps Lifecycle & Tools in AWS

Chapter 4: Getting Started with AWS

Lecture 1: What do we cover in this section ?

Lecture 2: Create AWS Account

Lecture 3: Setting up MFA on Root Account

Lecture 4: Create IAM Account and Account Alias

Lecture 5: Setup CLI with Credentials

Lecture 6: IAM Policy

Lecture 7: IAM Policy generator & attachment

Lecture 8: Delete the IAM User

Lecture 9: S3 Bucket and Storage Classes

Lecture 10: Creation of S3 Bucket from Console

Lecture 11: Creation of S3 Bucket from CLI

Lecture 12: Version Enablement in S3

Lecture 13: Introduction EC2 instances

Lecture 14: Launch EC2 instance & SSH into EC2 Instances

Lecture 15: Clean Up Activity

Chapter 5: Linux Operating System for DevOps and Data Scientists

Lecture 1: What do we learn in this section ?

Lecture 2: Linux Features & Bash

Lecture 3: How to Launch EC2 Instances (Quick Refresh)

Lecture 4: Linux Basic Commands

Chapter 6: Source code Management using GIT – CodeCommit

Lecture 1: Introduction to CI CD Pipeline

Lecture 2: Introduction to AWS Code Commit & DVCS

Lecture 3: Git Initial config & Git Commands

Lecture 4: Setting up the workspace for Git

Lecture 5: Git Workflow

Lecture 6: Adding files to Staging Area

Lecture 7: Staged Differences

Lecture 8: Git Unstage

Lecture 9: Git Reset & Revert

Lecture 10: Update on CodeCommit

Lecture 11: AWS Code Commit Remote Git Commands

Lecture 12: Cloning and Branching

Lecture 13: Git Branching Hands On Part 1

Lecture 14: Git Branching Hands On Part 2

Lecture 15: Git Conflicts & Resolving them

Lecture 16: Git Rebase Vs Git Merge

Lecture 17: Git Stash Introduction

Lecture 18: Git Stash Hands On

Lecture 19: AWS Code Commit Security

Lecture 20: AWS Code Commit Security – Hands On

Lecture 21: AWS Code Commit Integration – Triggers – Notifications – CloudWatch – EventBridg

Lecture 22: Summary

Chapter 7: YAML Crash Course

Lecture 1: YAML Crash Course

Chapter 8: AWS CodeBuild

Lecture 1: Introduction to AWS CodeBuild

Lecture 2: Create First CodeBuild Project

Lecture 3: buildspec.yml deep dive

Lecture 4: Code Build Hands On

Lecture 5: Environment Variables in CodeBuild & buildspec.yml deep dive Hands On

Lecture 6: Working CodeBuild Artifacts Hands On

Lecture 7: AWS CodeBuild Triggers

Lecture 8: CleanUp Activity

Chapter 9: AWS Code Deploy

Lecture 1: AWS CodeDeploy Introduction

Lecture 2: First AWS CodeDeploy – Intro to Hands On

Lecture 3: First AWS CodeDeploy

Lecture 4: appspec.yml – Deep Dive

Lecture 5: CodeDeploy Summary

Chapter 10: Code Pipeline

Lecture 1: AWS CodePipeline Introduction

Lecture 2: Create CodePepeline – Hands On

Lecture 3: Automatic CI CD Process with Manual Approval

Lecture 4: Summary & CleanUp

Chapter 11: Docker Containers

Lecture 1: Introduction to Docker

Lecture 2: Installation of Docker Desktop

Lecture 3: Docker Basics

Lecture 4: Pull the image from Docker Registry

Lecture 5: Dockerfile

Lecture 6: Push the Docker Image to ECR

Lecture 7: Hands On – Amazon ECR for AWS CodeBuild

Lecture 8: Summary

Chapter 12: Practical MLOps – Amazon Sagemaker

Lecture 1: What is AWS Sagemaker ?

Instructors

Manifold AI Learning ?
Learn the Future – Data Science, Machine Learning & AI

Rating Distribution

1 stars: 11 votes

2 stars: 12 votes

3 stars: 51 votes

4 stars: 199 votes

5 stars: 356 votes

Frequently Asked Questions

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