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:
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Design, build, deploy, optimize, train, tune, and maintain ML solutions using AWS Cloud.
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Adopt a Production Level mindset tailored for Machine Learning in conjunction with DevOps best practices.
**2. Beyond Basics:
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Employ model-training best practices on extensive cloud-based datasets.
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Demonstrate expertise in deployment best practices for consistent functionality.
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Implement operational best practices to guarantee zero downtime.
**3. Structured Learning Path:
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Follow a logical, structured path with in-depth explanations, practical exercises, and relevant demonstrations.
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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
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Get acquainted with the course objectives and the experienced instructor leading the way.
Section 2: Understanding MLOps
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Delve into the core concepts of MLOps, understanding its significance and application.
Section 3: DevOps Principles for Data Scientists
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Explore the principles of DevOps tailored for data scientists, bridging the gap between development and operations.
Section 4: Getting Started with AWS
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Acquaint yourself with the AWS platform, laying the foundation for subsequent sections.
Sections 5-16: In-Depth Exploration
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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:
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Data Ingestion and Collection
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Data Processing and ETL (Extract, Transform, Load)
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Data Analysis and Visualization
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Model Training and Deployment/Inference
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Operational Aspects of Machine Learning
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AWS Machine Learning Application Services
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Notebooks and Integrated Development Environments (IDEs)
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Version Control with AWS CodeCommit
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Amazon Athena, AWS Batch, Amazon EC2
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Amazon Elastic Container Registry (Amazon ECR), AWS Glue
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Amazon CloudWatch, AWS Lambda
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Amazon S3 for Storage and Scalability
Access to Course Materials:
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All course materials, including source code, are available on GitHub for convenient access from anywhere.
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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?
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Gain a Production Level mindset tailored for AI/ML in conjunction with DevOps practices.
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Acquire proficiency in deploying solutions on scalable datasets beyond personal laptops.
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Comprehensive exploration of AWS services crucial for MLOps.
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Real-world applications and hands-on projects for practical learning.
Your Success in MLOps Begins Here:
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Equip yourself with the latest tools and best practices on the AWS platform.
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Tackle complex business challenges with confidence.
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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
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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
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