Course Description

"Master Cloud MLOps: AWS SageMaker & Azure ML" is an innovative and comprehensive course designed to equip you with the latest skills in machine learning operations (MLOps) using two of the most prominent cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course offers a unique blend of theoretical knowledge and hands-on experience, ensuring you're well-prepared for the dynamic world of cloud-based machine learning.

What Students Will Learn

• End-to-end machine learning pipelines on AWS and Azure
• Data engineering and ML foundations on AWS
• Creation of data repositories, ETL pipelines, and serverless solutions
• Essential data science skills including data cleaning, visualization, and analysis
• Training, selection, and tuning of ML models using AWS SageMaker
• Best practices for operationalizing models in production environments
• Deployment and maintenance of ML solutions using both CPU and GPU instances
• Preparation for AWS and Azure ML certifications

Prerequisites

While the course is labeled as introductory level, having a basic understanding of programming (preferably Python) and fundamental concepts of machine learning would be beneficial. However, the course is designed to accommodate learners from various backgrounds, including data scientists, ML engineers, analysts, and cloud professionals.

Course Coverage

• Data Engineering with AWS Technology
• Exploratory Data Analysis with AWS Technology
• Modeling with AWS Technology
• MLOps with AWS Technology
• Machine Learning Certifications (including Azure)
• Hands-on projects and exercises throughout the course
• Preparation for AWS and Azure certifications

Who This Course Is For

• Data scientists looking to expand their cloud ML skills
• ML engineers aiming to enhance their knowledge of AWS and Azure
• Data analysts interested in moving into machine learning
• Cloud professionals wanting to specialize in ML operations
• Anyone aspiring to earn AWS and Azure ML certifications

Real-World Applications

The skills acquired in this course are directly applicable to real-world scenarios. Learners will be able to:

• Design and implement ML solutions for businesses using AWS and Azure
• Optimize and scale ML models in production environments
• Automate ML workflows for increased efficiency
• Manage and monitor ML models in cloud environments
• Contribute to data-driven decision-making processes in organizations
• Qualify for ML and cloud-related job roles in the industry
• Apply for AWS and Azure ML certifications to boost their career prospects

Syllabus

The course is divided into five comprehensive modules:

1. Data Engineering with AWS Technology (7 hours)
2. Exploratory Data Analysis with AWS Technology (7 hours)
3. Modeling with AWS Technology (7 hours)
4. MLOps with AWS Technology (5 hours)
5. Machine Learning Certifications (4 hours)

Each module contains a mix of video lectures, readings, quizzes, and hands-on labs, ensuring a well-rounded learning experience. The course also includes ungraded labs and optional discussion prompts to enhance learning and foster community interaction.