Course content

1: ML Problem Framing

1.1 Translate business challenge into ML use case

• Defining business problems
• Identifying nonML solutions
• Defining output use
• Managing incorrect results
• Identifying data sources

1.2 Define ML problem

• Defining problem type (classification, regression, clustering, etc.)
• Defining outcome of model predictions
• Defining the input (features) and predicted output format

1.3 Define business success criteria

• Success metrics
• Key results
• Determination of when a model is deemed unsuccessful

1.4 Identify risks to feasibility and implementation of ML solution

• Assessing and communicating business impact
• Assessing ML solution readiness
• Assessing data readiness
• Aligning with Google AI principles and practices (e.g. different biases)

2: ML Solution Architecture

2.1 Design reliable, scalable, highly available ML solutions

• Optimizing data use and storage
• Data connections
• Automation of data preparation and model training/deployment
• SDLC best practices

2.2 Choose appropriate Google Cloud software components

• A variety of component types – data collection; data management
• Exploration/analysis
• Feature engineering
• Logging/management
• Automation
• Monitoring
• Serving

2.3 Choose appropriate Google Cloud hardware components

• Selection of quotas and compute/accelerators with components

2.4 Design architecture that complies with regulatory and security concerns

• Building secure ML systems
• Privacy implications of data usage
• Identifying potential regulatory issues

3: Data Preparation and Processing

3.1 Data ingestion

• Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
• Database migration
• Streaming data (e.g. from IoT devices)

3.2 Data exploration (EDA)

• Visualization
• Statistical fundamentals at scale
• Evaluation of data quality and feasibility

3.3 Design data pipelines

• Batching and streaming data pipelines at scale
• Data privacy and compliance
• Monitoring/changing deployed pipelines

3.4 Build data pipelines

• Data validation
• Handling missing data
• Handling outliers
• Managing large samples (TFRecords)
• Transformations (TensorFlow Transform)

3.5 Feature engineering

• Data leakage and augmentation
• Encoding structured data types
• Feature selection
• Class imbalance
• Feature crosses

4: ML Model Development

4.1 Build a model

• Choice of framework and model
• Modeling techniques given interpretability requirements
• Transfer learning
• Model generalization
• Overfitting

4.2 Train a model

• Productionizing
• Training a model as a job in different environments
• Tracking metrics during training
• Retraining/redeployment evaluation

4.3 Test a model

• Unit tests for model training and serving
• Model performance against baselines, simpler models, and across the time dimension
• Model explainability on Cloud AI Platform

4.4 Scale model training and serving

• Distributed training
• Hardware accelerators
• Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)

5: ML Pipeline Automation & Orchestration

5.1 Design pipeline

• Identification of components, parameters, triggers, and compute needs
• Orchestration framework
• Hybrid or multi-cloud strategies

5.2 Implement training pipeline

• Decoupling components with Cloud Build
• Constructing and testing of parameterized pipeline definition in SDK
• Tuning compute performance
• Performing data validation
• Storing data and generated artifacts

5.3 Implement serving pipeline

• Model binary options
• Google Cloud serving options
• Testing for target performance
• Setup of trigger and pipeline schedule

5.4 Track and audit metadata

• Organization and tracking experiments and pipeline runs
• Hooking into model and dataset versioning
• Model/dataset lineage

5.5 Use CI/CD to test and deploy models

• Hooking modes into existing CI/CD deployment system
• AB and Canary testing

6: ML Solution Monitoring, Optimization, and Maintenance

6.1 Monitor ML solutions

• Performance and business quality of ML model predictions
• Logging strategies
• Establishing continuous evaluation metrics

6.2 Troubleshoot ML solutions

• Permission issues (IAM)
• Common training and serving errors (TensorFlow)
• ML system failure and biases

6.3 Tune performance of ML solutions for training & serving in production

• Optimization and simplification of input pipeline for training
• Simplification techniques
• Identification of appropriate retraining policy

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