Habeshapros

Course Summary

The AWS Certified Machine Learning Engineer – Associate Course is designed to prepare you for the AWS Certified Machine Learning Engineer (MLA-C01) exam. This course provides practical training on designing, building, deploying, and managing machine learning solutions on AWS. Covering the entire ML pipeline, from data preparation to model deployment and monitoring, this course will equip you with the skills to operationalize ML models at scale. This course is ideal for those with some background in AWS and machine learning who want to advance their knowledge and achieve certification.

Course Details

• Course Price: $1,400
• Total Duration: 12 weeks
• Class Schedule: Twice a week (1 hour and 30 minutes per session)
• Total Weekly Hours: 3 hours
• Mode: Live, instructor-led classes (Online)
• Platform: Zoom for classes and Slack for communication, assignments, and discussions

What You Will Learn

1. Data Preparation for Machine Learning (28% of Exam Content)

• Ingestion and Storage: Handling various data formats (Parquet, JSON, CSV), using AWS storage options like S3 and EFS, and ingesting data using Amazon Kinesis
• Data Transformation and Feature Engineering: Cleaning and transforming data, feature scaling, encoding techniques, and using tools like SageMaker Data Wrangler and AWS Glue
• Data Quality and Bias Mitigation: Identifying and mitigating data bias, data quality checks with SageMaker Clarify, and compliance with regulatory requirements

2. ML Model Development (26% of Exam Content)

• Model Selection and Training: Choosing appropriate algorithms, using SageMaker’s built-in models, and selecting foundation models through Amazon Bedrock
• Hyperparameter Tuning and Optimization: Applying tuning techniques such as random search and Bayesian optimization with SageMaker’s automatic tuning tools
• Model Evaluation: Analyzing model performance with metrics like accuracy, precision, recall, and using SageMaker Model Debugger for debugging

3. Deployment and Orchestration of ML Workflows (22% of Exam Content)

• Deployment Infrastructure: Selecting deployment endpoints (real-time, batch, or serverless), configuring SageMaker endpoints, and choosing compute resources based on workload requirements
• Continuous Integration and Continuous Deployment (CI/CD): Implementing CI/CD pipelines using AWS CodePipeline, CodeBuild, and CodeDeploy, and applying orchestration with SageMaker Pipelines
• Deployment Strategies: Rolling out ML models with deployment strategies like blue/green and canary deployments for safe releases

4. ML Solution Monitoring, Maintenance, and Security (24% of Exam Content)

• Monitoring ML Models: Detecting data and model drift, monitoring model health with SageMaker Model Monitor, and setting up dashboards with CloudWatch
• Cost Optimization and Infrastructure Management: Managing costs with AWS Cost Explorer and setting up auto-scaling for endpoints
• Security and Compliance: Applying IAM roles, bucket policies, and encryption for secure ML systems, and troubleshooting security issues in the ML pipeline

Learning Outcomes

• Understand the complete ML pipeline from data ingestion to model deployment and monitoring.
• Develop skills in model training, hyperparameter tuning, and debugging using AWS tools.
• Automate ML workflows using CI/CD and SageMaker Pipelines.
• Implement robust monitoring, maintenance, and security practices for ML solutions.
• Be well-prepared for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam.

Course Schedule Overview

• Weeks 1-3: Data Preparation for Machine Learning
• Weeks 4-6: ML Model Development
• Weeks 7-9: Deployment and Orchestration of ML Workflows
• Weeks 10-12: ML Solution Monitoring, Maintenance, and Security

Frequently Asked Questions (FAQs)

1. How long will the course take to complete?
The course takes 12 weeks with 2 sessions per week.

2. Do I need prior experience in ML or AWS?
Yes, this course is intended for individuals with at least 1 year of experience using AWS for ML engineering or in a related field.

3. What tools do I need?
You’ll need a computer with internet access. Access to an AWS account is also required, and we’ll guide you through setting up necessary resources.

4. Will the classes be recorded?
Yes, all sessions are recorded, and you’ll have lifetime access to the recordings.

5. What happens if I miss a class?
You can catch up with the recorded session, and instructors are available for follow-up questions on Slack.

6. Will I receive a certificate?
Yes, you’ll receive a certificate of completion, and this course prepares you for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) certification exam.

7. Are there hands-on projects?
Yes, each domain includes practical assignments to apply the skills learned, with hands-on experience in building, deploying, and monitoring ML models on AWS.

Why Choose This Course?

• AWS-Approved Curriculum: Designed around the AWS Machine Learning Engineer – Associate (MLA-C01) exam guide.
• Hands-On Learning: Includes practical exercises and projects to apply your skills.
• Flexible Learning: Lifetime access to recorded sessions.
• Career Preparation: Be equipped for a career in ML engineering with AWS.

Enroll Now for $1,400

Advance your machine learning career with AWS. Start your journey toward becoming a certified AWS Machine Learning Engineer – Associate.