AWS Certified Machine Learning MLS-C01 Prep

Real case study analysis: Setting up a serverless ETL job to prepare data for model training.

Learn best practices for data lakes, partitioning strategies, and querying data efficiently.

Problem-solving session: Deciding when to use Kinesis, Glue, or Batch for your ML data needs.

How to use SageMaker Processing Jobs and Scikit-learn for scalable feature transformation.

A practical guide to orchestrating complex data workflows and handling failures gracefully.

Walkthrough of setting up a labeling job and managing human-in-the-loop workflows.

Techniques for preparing massive datasets for training, including sharding and format conversion.

Implementing automated checks to ensure your data is clean and ready for modeling.

Comprehensive setup and overview of the SageMaker Studio environment for interactive analysis.

How to create insightful dashboards to understand distributions and identify outliers.

A no-code approach to profiling your data and identifying anomalies before modeling.

Practical strategies for imputation and handling null values in a cloud environment.

Applying StandardScaler and MinMaxScaler using SageMaker processing scripts.

When to use One-Hot, Label, or Target encoding for your specific ML problem.

Tools and techniques to analyze your dataset for fairness and class imbalance issues.

Decision framework for selecting XGBoost, Linear Learner, or bringing your own script.

Step-by-step guide to configuring and running hyperparameter optimization jobs.

Practical walkthrough of model training across multiple instances for speed.

Understanding Precision, Recall, F1-score, and AUC in the context of AWS model evaluation.

How to log parameters, metrics, and artifacts to keep your experiments organized.

Real-time debugging of training jobs to catch issues like vanishing gradients.

Using SHAP values to understand feature importance and explain model predictions.

Best practices for organizing and managing your trained model artifacts.

Implementing k-fold validation to ensure your model generalizes well.

Deploying a model to a live endpoint and testing it with sample data.

How to process huge amounts of data asynchronously without a live endpoint.

Strategies for safely rolling out new model versions to production users.

Configuring scaling policies to handle traffic spikes and save costs during quiet periods.

Setting up alerts to detect when your model's performance degrades over time.

Automating the training, evaluation, and deployment process from code commit to production.

Implementing VPCs, IAM policies, and HTTPS for secure model access.

A problem-solving session on diagnosing and fixing endpoint errors.

Choosing the right instance types and using Inference Recommender to lower bills.

How to optimize and deploy models to edge devices like cameras and industrial machines.

Putting it all together: A complete project from data ingestion to a live, monitored endpoint.

A quick review of what to expect on exam day and common mistakes to avoid.