Why this course?

Description

encountered in the domains of Amazon Web Services (AWS), Machine Learning (ML), and Machine Learning Operations (MLOps). The questions are selected based on industry relevance, recurring themes in technical interviews, and comprehensive coverage of core topics taught throughout this curriculum.

Chapter Objectives

• Reaffirm their conceptual understanding of AWS, ML, and MLOps.
• Develop confidence in responding to technical and scenario-based questions.
• Prepare effectively for interviews in roles such as Cloud Engineer, Data Scientist, ML Engineer, and MLOps Engineer.
• Identify knowledge gaps and areas that require further review or practice.

Structure of the Interview Questions
1. AWS Cloud Computing Essentials

• Cloud architecture and service models
• Core AWS services (EC2, S3, RDS, Lambda, etc.)
• Networking, Identity & Access Management (IAM), and security practices
• Cost optimization and scalability strategies

2. Machine Learning Fundamentals

• Types of learning: supervised, unsupervised, reinforcement
• Preprocessing: encoding, handling nulls, scaling
• Model selection, training, and evaluation
• Pandas, NumPy, Matplotlib, Seaborn for data analysis and visualization

3. Tools for ML with AWS

• Amazon SageMaker Studio and Notebooks
• AWS Glue and ETL pipeline basics
• Integration of data sources (S3, DynamoDB, RDS) with ML workflows
• Introduction to SageMaker Autopilot and built-in algorithms

4. MLOps and DevOps Practices

• CI/CD pipelines for machine learning projects
• Git & GitHub usage in ML lifecycle
• Deployment strategies: batch inference, real-time endpoints
• Monitoring ML models in production (CloudWatch, Model Drift)