Google Professional Machine Learning Engineer Certification
A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.
Section 1: Framing ML problems
1.1 Translating business challenges into ML use cases. Considerations include:
Choosing the best solution (ML vs. non-ML, custom vs. pre-packaged [e.g., AutoML, Vision API]) based on the business requirements
Defining how the model output should be used to solve the business problem
Deciding how incorrect results should be handled
Identifying data sources (available vs. ideal)
1.2 Defining ML problems. Considerations include:
Problem type (e.g., classification, regression, clustering)
Outcome of model predictions
Input (features) and predicted output format
1.3 Defining business success criteria. Considerations include:
Alignment of ML success metrics to the business problem
Key results
Determining when a model is deemed unsuccessful
1.4 Identifying risks to feasibility of ML solutions. Considerations include:
Assessing and communicating business impact
Assessing ML solution readiness
Assessing data readiness and potential limitations
Aligning with Google's Responsible AI practices (e.g., different biases)
Section 2: Architecting ML solutions
2.1 Designing reliable, scalable, and highly available ML solutions. Considerations include:
Choosing appropriate ML services for the use case (e.g., Cloud Build, Kubeflow)
Component types (e.g., data collection, data management)
Exploration/analysis
Feature engineering
Logging/management
Automation
Orchestration
Monitoring
Serving
2.2 Choosing appropriate Google Cloud hardware components. Considerations include:
Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)
2.3 Designing architecture that complies with security concerns across sectors/industries. Considerations include:
Building secure ML systems (e.g., protecting against unintentional exploitation of data/model, hacking)
Privacy implications of data usage and/or collection (e.g., handling sensitive data such as Personally Identifiable Information [PII] and Protected Health Information [PHI])
Section 3: Designing data preparation and processing systems
3.1 Exploring data (EDA). Considerations include:
Visualization
Statistical fundamentals at scale
Evaluation of data quality and feasibility
Establishing data constraints (e.g., TFDV)
3.2 Building data pipelines. Considerations include:
Organizing and optimizing training datasets
Data validation
Handling missing data
Handling outliers
Data leakage
3.3 Creating input features (feature engineering). Considerations include:
Ensuring consistent data pre-processing between training and serving
Encoding structured data types
Feature selection
Class imbalance
Feature crosses
Transformations (TensorFlow Transform)
Section 4: Developing ML models
4.1 Building models. Considerations include:
Choice of framework and model
Modeling techniques given interpretability requirements
Transfer learning
Data augmentation
Semi-supervised learning
Model generalization and strategies to handle overfitting and underfitting
4.2 Training models. Considerations include:
Ingestion of various file types into training (e.g., CSV, JSON, IMG, parquet or databases, Hadoop/Spark)
Training a model as a job in different environments
Hyperparameter tuning
Tracking metrics during training
Retraining/redeployment evaluation
4.3 Testing models. Considerations include:
Unit tests for model training and serving
Model performance against baselines, simpler models, and across the time dimension
Model explainability on AI Platform
4.4 Scaling model training and serving. Considerations include:
Distributed training
Scaling prediction service (e.g., AI Platform Prediction, containerized serving)
Section 5: Automating and orchestrating ML pipelines
5.1 Designing and implementing training pipelines. Considerations include:
Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
Orchestration framework (e.g., Kubeflow Pipelines/AI Platform Pipelines, Cloud Composer/Apache Airflow)
Hybrid or multicloud strategies
System design with TFX components/Kubeflow DSL
5.2 Implementing serving pipelines. Considerations include:
Serving (online, batch, caching)
Google Cloud serving options
Testing for target performance
Configuring trigger and pipeline schedules
5.3 Tracking and auditing metadata. Considerations include:
Organizing and tracking experiments and pipeline runs
Hooking into model and dataset versioning
Model/dataset lineage
Section 6: Monitoring, optimizing, and maintaining ML solutions
6.1 Monitoring and troubleshooting ML solutions. Considerations include:
Performance and business quality of ML model predictions
Logging strategies
Establishing continuous evaluation metrics (e.g., evaluation of drift or bias)
Understanding Google Cloud permissions model
Identification of appropriate retraining policy
Common training and serving errors (TensorFlow)
ML model failure and resulting biases
6.2 Tuning performance of ML solutions for training and serving in production. Considerations include:
Optimization and simplification of input pipeline for training
Simplification techniques
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