Domain 1: Data Engineering

• Create data repositories for machine learning.
• Identify data sources (e.g., content and location, primary sources such as user data)
• Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)
• Identify and implement a data ingestion solution.
• Data job styles/types (batch load, streaming)
• Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads)
• Kinesis
• Kinesis Analytics
• Kinesis Firehose
• EMR
• Glue
• Job scheduling
• Identify and implement a data transformation solution.
• Transforming data transit (ETL: Glue, EMR, AWS Batch)
• Handle ML-specific data using map reduce (Hadoop, Spark, Hive)

Domain 2: Exploratory Data Analysis

• Sanitize and prepare data for modeling.
• Identify and handle missing data, corrupt data, stop words, etc.
• Formatting, normalizing, augmenting, and scaling data
• Labeled data (recognizing when you have enough labeled data and identifying mitigation
• strategies [Data labeling tools (Mechanical Turk, manual labor)])
• Perform feature engineering.
• Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc.
• Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic
• features, 1 hot encoding, reducing dimensionality of data)
• Analyze and visualize data for machine learning.
• Graphing (scatter plot, time series, histogram, box plot)
• Interpreting descriptive statistics (correlation, summary statistics, p value)
• Clustering (hierarchical, diagnosing, elbow plot, cluster size)

Domain 3: Modeling

• Frame business problems as machine learning problems.
• Determine when tuse/when not tuse ML
• Know the difference between supervised and unsupervised learning
• Selecting from among classification, regression, forecasting, clustering, recommendation, etc.
• Select the appropriate model(s) for a given machine learning problem.
• Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN,
• CNN, Ensemble, Transfer learning
• Express intuition behind models
• Train machine learning models.
• Train validation test split, cross-validation
• Optimizer, gradient descent, loss functions, local minima, convergence, batches, probability,
• Compute choice (GPU vs. CPU, distributed vs. non-distributed, platform [Spark vs. non-Spark])
• Model updates and retraining
• Batch vs. real-time/online
• Perform hyperparameter optimization.
• Regularization
• Drop out
• L1/L2
• Cross validation
• Model initialization
• Neural network architecture (layers/nodes), learning rate, activation functions
• Tree-based models (# of trees, # of levels)
• Linear models (learning rate)
• Evaluate machine learning models.
• Avoid overfitting/underfitting (detect and handle bias and variance)
• Metrics (AUC-ROC, accuracy, precision, recall, RMSE, F1 score)
• Confusion matrix
• Offline and online model evaluation, A/B testing
• Compare models using metrics (time ttrain a model, quality of model, engineering costs)
• Cross validation

Domain 4: Machine Learning Implementation and Operations

• Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
• AWS environment logging and monitoring
• CloudTrail and CloudWatch
• Build error monitoring
• Multiple regions, Multiple AZs
• AMI/golden image
• Docker containers
• AutScaling groups
• Rightsizing
• Instances
• Provisioned IOPS
• Volumes
• Load balancing
• AWS best practices
• Recommend and implement the appropriate machine learning services and features for a given problem.
• ML on AWS (application services)
• Poly
• Lex
• Transcribe
• AWS service limits
• Build your own model vs. SageMaker built-in algorithms
• Infrastructure: (spot, instance types), cost considerations
• Using spot instances ttrain deep learning models using AWS Batch
• Apply basic AWS security practices tmachine learning solutions.
• IAM
• S3 bucket policies
• Security groups
• VPC
• Encryption/anonymization
• Deploy and operationalize machine learning solutions.
• Exposing endpoints and interacting with them
• ML model versioning
• A/B testing
• Retrain pipelines
• ML debugging/troubleshooting
• Detect and mitigate drop in performance
• Monitor performance of the model