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
To see the full course content Download now
Course Prerequisites
- Basic understanding of cloud concepts
- Experience writing Python code
Who can attend
- 3+ years of industry experience including 1+ years designing and managing solutions using GCP.
Number of Hours: 40hrs
Certification
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