IA Machine and Deep Learning AWS Services Fundamentals

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 Analytics

• Kinesis Firehose

• EMR o 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 modelling.

• 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
   

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 to use/when not to use 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 o Batch vs. real-time/online
   

 Perform hyperparameter optimization.

• Regularization Drop out

• 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 to train a model, quality of model, engineering costs)

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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 o Build error monitoring

• Multiple regions, Multiple AZs

• AMI/golden image

• Docker containers

• Auto Scaling groups

• Rightsizing o Instances o Provisioned IOPS o Volumes

• Load balancing

• AWS best practices
   

Recommend and implement the appropriate machine learning services and features

• ML on AWS (application services) and Transcribe

• AWS service Limits

• Build your own model vs. Sage Maker built-in algorithms