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AWS Machine Learning Course content
Total Duration - 50 Hrs.
Domain 1: Data Engineering
Task Statement 1.1: Create data repositories for ML.
• Identify data sources (for example, content and location, primary sources such as user data).
• Determine storage mediums (for example, databases, Amazon S3, Amazon

Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS]).
Task Statement 1.2: Identify and implement a data ingestion solution.
• Identify data job styles and job types (for example, batch load, streaming).
• Orchestrate data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads).
  o Amazon Kinesis
  o Amazon Kinesis Data Firehose
  o Amazon EMR
   o AWS Glue
   o Amazon Managed Service for Apache Flink
• Schedule jobs.

Task Statement 1.3: Identify and implement a data transformation solution.
• Transform data in transit (ETL, AWS Glue, Amazon EMR, AWS Batch).
• Handle ML-specific data by using MapReduce (for example, Apache Hadoop,Apache Spark, Apache Hive).

Domain 2: Exploratory Data Analysis
Task Statement 2.1: Sanitize and prepare data for modeling.
• Identify and handle missing data, corrupt data, and stop words.
• Format, normalize, augment, and scale data.
• Determine whether there is sufficient labeled data.
   o Identify mitigation strategies.
   o Use data labelling tools (for example, Amazon Mechanical Turk).


Task Statement 2.2: Perform feature engineering.
• Identify and extract features from datasets, including from data sources such as text, speech, image, public datasets.
• Analyze and evaluate feature engineering concepts (for example, binning, tokenization, outliers, synthetic features, one-hot encoding, reducing dimensionality of data).
Task Statement 2.3: Analyze and visualize data for ML.
• Create graphs (for example, scatter plots, time series, histograms, box plots).
• Interpret descriptive statistics (for example, correlation, summary statistics,p-value).
• Perform cluster analysis (for example, hierarchical, diagnosis, elbow plot,cluster size).


Domain 3: Modeling
Task Statement 3.1: Frame business problems as ML problems.
• Determine when to use and when not to use ML.
• Know the difference between supervised and unsupervised learning.
• Select from among classification, regression, forecasting, clustering, and recommendation models.

Task Statement 3.2: Select the appropriate model(s) for a given ML problem.
• XGBoost, logistic regression, k-means, linear regression, decision trees,
random forests, RNN, CNN, ensemble, transfer learning
• Express the intuition behind models.

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Task Statement 3.3: Train ML models.
• Split data between training and validation (for example, cross validation).
• Understand optimization techniques for ML training (for example, gradient decent, loss functions, convergence).
• Choose appropriate compute resources (for example GPU or CPU, distributed or non-distributed).
  o Choose appropriate compute platforms (Spark or non-Spark).
• Update and retrain models.
  o Batch or real-time/online


Task Statement 3.4: Perform hyperparameter optimization.
• Perform regularization.
  o Drop out
  o L1/L2
• Perform cross validation.
• Initialize models.
• Understand neural network architecture (layers and nodes), learning rate, and activation functions.
• Understand tree-based models (number of trees, number of levels).
• Understand linear models (learning rate).

Task Statement 3.5: Evaluate ML models.
• Avoid overfitting or underfitting.
  o Detect and handle bias and variance.
• Evaluate metrics (area under curve [AUC]-receiver operating characteristics [ROC], accuracy, precision, recall, Root Mean Square Error [RMSE], F1 score).
• Interpret confusion matrices.
• Perform offline and online model evaluation (A/B testing).
• Compare models by using metrics (for example, time to train a model,quality of model, engineering costs).
• Perform cross validation.

Domain 4: Machine Learning Implementation and Operations
Task Statement 4.1: Build ML solutions for performance, availability, scalability,resiliency, and fault tolerance.
• Log and monitor AWS environments.
  o AWS CloudTrail and Amazon CloudWatch
  o Build error monitoring solutions.
• Deploy to multiple AWS Regions and multiple Availability Zones.
• Create AMIs and golden images.
• Create Docker containers.
• Deploy Auto Scaling groups.
• Rightsize resources (for example, instances, Provisioned IOPS, volumes).
• Perform load balancing.
• Follow AWS best practices.

Task Statement 4.2: Recommend and implement the appropriate ML services and features for a given problem.
• ML on AWS (application services)
  o Amazon Polly
  o Amazon Lex
  o Amazon Transcribe
• Understand AWS service quotas.
• Determine when to build custom models and when to use Amazon SageMaker built-in algorithms.
• Understand AWS infrastructure (for example, instance types) and cost considerations.
  o Use Spot Instances to train deep learning models by using AWS Batch.

Task Statement 4.3: Apply basic AWS security practices to ML solutions.
• AWS Identity and Access Management (IAM)
• S3 bucket policies
• Security groups
• VPCs
• Encryption and anonymization

Task Statement 4.4: Deploy and operationalize ML solutions.
• Expose endpoints and interact with them.
• Understand ML models.
• Perform A/B testing.
• Retrain pipelines.
• Debug and troubleshoot ML models.
  o Detect and mitigate drops in performance.
  o Monitor performance of the model.