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This course treats machine learning delivery as a production engineering problem. It moves through AWS build and deployment services, container images, SageMaker and its pipelines, and the data services that feed training, such as Glue and Athena. Course materials and source code are kept on GitHub.
Data scientists who need to ship models and DevOps engineers who need to understand ML workloads can both find something useful here. Expect operational topics such as CloudWatch and Lambda alongside the modeling work.
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!
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