PDF Version DemoProfessional Machine Learning Engineer - Google Certification Path
The associate level certification is focused on the fundamental skills of deploying, monitoring, and maintaining projects on Google Cloud. This certification is a good starting point for those new to cloud and can be used as a path to professional level certifications.
Professional certifications span key technical job functions and assess advanced skills in design, implementation, and management. These certifications are recommended for individuals with industry experience and familiarity with Google Cloud products and solutions.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Who should take the Professional Machine Learning Engineer - Google
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 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 application development, infrastructure management, data engineering, and security. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, they design and create scalable solutions for optimal performance.
The Google Professional-Machine-Learning-Engineer exam is for entry-level IT specialists and organization professionals with standard knowledge of the Google platform. The Google CCP certification validates the potential client's understanding of these topics and their skills; standard building principles, key services and also their use cases, security, and protection, as well as compliance with the Google model, paid versions, and prices. Google Professional-Machine-Learning-Engineer exam is the appropriate starting point for Google certification and is also an excellent resource for those interested in non-technical projects.
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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google Data Preparation and Processing
The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:
- Streaming data (e.g. from IoT devices)
- Feature engineering
- Ingestion of various file types (e.g. Csv, json, img, parquet or databases, Hadoop/Spark)
- Data privacy and compliance
- Visualization
- Data ingestion
- Database migration
- Build data pipelines
- Transformations (TensorFlow Transform)
- Data leakage and augmentation
- Batching and streaming data pipelines at scale
- Data validation
- Design data pipelines
- Encoding structured data types
- Handling missing data
- Feature crosses
- Statistical fundamentals at scale
- Evaluation of data quality and feasibility
- Class imbalance
- Handling outliers
- Data exploration (EDA)
- Monitoring/changing deployed pipelines
- Feature selection
- Managing large samples (TFRecords)
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Architecting low-code ML solutions | - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models - AutoML capabilities and implementation |
| Automating and orchestrating ML pipelines | - Vertex AI Pipelines (Kubeflow Pipelines) - Triggering and scheduling pipelines - CI/CD for ML systems |
| Monitoring ML solutions | - Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring) |
| Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) - Hyperparameter tuning |
| Serving and scaling models | - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Batch prediction |
| Collaborating within and across teams to manage data and models | - Data management and governance - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) |
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