AWS Certified Machine Learning - Specialty (MLS-C01) Prep

Real case study analysis: Building scalable data pipelines using Kinesis, Glue, and S3 for real-time and batch processing.

We'll compare different data storage options and discuss best practices for organizing data for ML training.

Step-by-step guide to using AWS Glue for ETL, including handling missing values and feature engineering basics.

Learn how to use SageMaker Processing Jobs and Feature Store to create, track, and serve high-quality features.

Deep dive into processing massive datasets using Spark on EMR or within SageMaker, a common exam scenario.

A focused lesson on implementing encryption at rest and in transit, and managing access for data pipelines.

Practical walkthrough of using SageMaker Data Wrangler to automate data quality checks and analysis.

We'll analyze a scenario where data format and location impact training performance and choose the optimal solution.

A hands-on lab where you'll take a messy dataset and build a complete data prep pipeline using AWS services.

Step-by-step guide to using algorithms like XGBoost and Linear Learner for common ML problems like classification.

Learn how to package your custom training code (e.g., PyTorch, TensorFlow) into a Docker container for SageMaker.

We'll explore how to set up and manage hyperparameter tuning jobs to find the best model configuration efficiently.

Understand when and how to use SageMaker's distributed training features to speed up training on large datasets.

A conceptual but practical lesson on splitting your data correctly to avoid overfitting and ensure model generalizability.

Learn to use SageMaker Debugger to identify issues like vanishing gradients and bottlenecks during training.

Problem-solving session: We'll match model complexity and data size to the most cost-effective EC2 instance types.

Explore how to leverage built-in algorithms and pre-trained models from AWS Marketplace to accelerate development.

A complete walkthrough of deploying a trained model to a real-time endpoint for inference.

Learn how to safely roll out new models using SageMaker's deployment strategies to minimize risk.

Step-by-step guide to setting up batch transform jobs for large-scale, offline predictions.

We'll cover techniques like model compilation with SageMaker Neo and choosing the right instance types for inference.

Real case study analysis: Building a repeatable, automated pipeline for data prep, training, and deployment.

Learn to use SageMaker Model Monitor to detect data and concept drift in production.

Problem-solving session: Architecting a CI/CD pipeline for your ML models using CodePipeline and CodeBuild.

A quick guide to using the Inference Recommender to find the best deployment configuration for your model.

Introduction to deploying and managing models on edge devices, a key topic for IoT scenarios.

Deep dive into VPC configurations, endpoint policies, and data encryption for deployed models.

A comprehensive lab where you'll train a model, deploy it, test it, and clean up all resources.

We'll explore Rekognition's custom labels feature and when to use it versus building a custom CV model.

Understanding Comprehend's capabilities for sentiment analysis, entity recognition, and custom classification.

A practical look at building personalization engines using Personalize and other SageMaker solutions.

Learn to use SageMaker's DeepAR and other forecasting algorithms for demand planning and forecasting.

Problem-solving session: Identifying fraudulent activity or system failures using SageMaker and other AWS tools.

When to use turnkey AI services (Lex, Polly, Translate) versus building custom ML solutions.

A critical discussion on bias detection, explainability (SageMaker Clarify), and responsible AI practices.

A timed mock exam with detailed explanations for each question to solidify your knowledge and identify weak spots.

We'll review tricky topics, common misinterpretations of exam questions, and key service limits to remember.

A final checklist and actionable plan for the day before and the day of your exam to maximize your confidence.