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The Google Cloud Professional Google Professional Machine Learning Engineer certification was ranked one of the top paying IT certifications of Global Knowledge.
This program provides the skills you need to advance your career as a professional Google Professional Machine Learning Engineer and recommends training to support your preparation for the industry-recognized Google Cloud Professional Google Professional Machine Learning Engineer certification.
You'll have the opportunity to deploy solution elements, including infrastructure components such as networks, systems and applications services, and you'll gain real world experience through a number of hands-on Qwiklabs projects.
Upon successful completion of this program, you will earn a certificate of completion to share with your professional network and potential employers.
If you would like to become Google Cloud certified and demonstrate your proficiency in the understanding of cloud architecture and Google Cloud Platform, design, develop, and manage solutions to drive business objectives, you will need to register for, and pass the official Google Cloud certification exam. You can find more details on how to register and additional resources to support your preparation at Nux software solutions.
- 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)
- Problem type (e.g., classification, regression, clustering)
- Outcome of model predictions
- Input (features) and predicted output format
- Alignment of ML success metrics to the business problem
- Key results
- Determining when a model is deemed unsuccessful
- 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)
- 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
- Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)
- 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])
- Visualization
- Statistical fundamentals at scale
- Evaluation of data quality and feasibility
- Establishing data constraints (e.g., TFDV)
- Organizing and optimizing training datasets
- Data validation
- Handling missing data
- Handling outliers
- Data leakage
- Ensuring consistent data pre-processing between training and serving
- Encoding structured data types
- Feature selection
- Class imbalance
- Feature crosses
- Transformations (TensorFlow Transform)
- 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
- 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
- Unit tests for model training and serving
- Model performance against baselines, simpler models, and across the time dimension
- Model explainability on Vertex AI
- Distributed training
- Scaling prediction service (e.g., Vertex AI Prediction, containerized serving)
- Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
- Orchestration framework (e.g., Kubeflow Pipelines/Vertex AI Pipelines, Cloud Composer/Apache Airflow)
- Hybrid or multicloud strategies
- System design with TFX components/Kubeflow DSL
- Serving (online, batch, caching)
- Google Cloud serving options
- Testing for target performance
- Configuring trigger and pipeline schedules
- Organizing and tracking experiments and pipeline runs
- Hooking into model and dataset versioning
- Model/dataset lineage
- 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
- Optimization and simplification of input pipeline for training
- Simplification techniques
