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Professional Machine Learning Engineer

Certification exam guide

A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes ML models by using Google Cloud technologies and knowledge of proven models and techniques. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer considers responsible AI and fairness throughout the ML model development process, and collaborates closely with other job roles to ensure long-term success of ML-based applications. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools. The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer makes ML accessible and enables teams across the organization. By training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable, performant solutions.

*Note: The exam does not directly assess coding skill. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.

The Professional Machine Learning Engineer exam does not cover generative AI, as the tools used to develop generative AI-based solutions are evolving quickly. If you are interested in generative AI, please refer to the Introduction to Generative AI Learning Path (all audiences) or the Generative AI for Developers Learning Path (technical audience). If you are a partner, please refer to the Gen AI partner courses: Introduction to Generative AI Learning Path, Generative AI for ML Engineers, and Generative AI for Developers.

Section 1: Architecting low-code ML solutions (~12% of the exam)

1.1 Developing ML models by using BigQuery ML. Considerations include:

    ●  Building the appropriate BigQuery ML model (e.g., linear and binary classification, regression, time-series, matrix factorization, boosted trees, autoencoders) based on the business problem

    ●  Feature engineering or selection by using BigQuery ML

    ●  Generating predictions by using BigQuery ML

1.2 Building AI solutions by using ML APIs. Considerations include:

    ●  Building applications by using ML APIs (e.g., Cloud Vision API, Natural Language API, Cloud Speech API, Translation)

    ●  Building applications by using industry-specific APIs (e.g., Document AI API, Retail API)

1.3 Training models by using AutoML. Considerations include:

    ●  Preparing data for AutoML (e.g., feature selection, data labeling, Tabular Workflows on AutoML)

    ●  Using available data (e.g., tabular, text, speech, images, videos) to train custom models

    ●  Using AutoML for tabular data

    ●  Creating forecasting models using AutoML

    ●  Configuring and debugging trained models

Section 2: Collaborating within and across teams to manage data and models (~16% of the exam)

2.1 Exploring and preprocessing organization-wide data (e.g., Cloud Storage, BigQuery, Cloud Spanner, Cloud SQL, Apache Spark, Apache Hadoop). Considerations include:

    ●  Organizing different types of data (e.g., tabular, text, speech, images, videos) for efficient training

    ●  Managing datasets in Vertex AI

    ●  Data preprocessing (e.g., Dataflow, TensorFlow Extended [TFX], BigQuery)

    ●  Creating and consolidating features in Vertex AI Feature Store

    ●  Privacy implications of data usage and/or collection (e.g., handling sensitive data such as personally identifiable information [PII] and protected health information [PHI])

2.2 Model prototyping using Jupyter notebooks. Considerations include:

    ●  Choosing the appropriate Jupyter backend on Google Cloud (e.g., Vertex AI Workbench, notebooks on Dataproc)

    ●  Applying security best practices in Vertex AI Workbench

    ●  Using Spark kernels

    ●  Integration with code source repositories

    ●  Developing models in Vertex AI Workbench by using common frameworks (e.g., TensorFlow, PyTorch, sklearn, Spark, JAX)

2.3 Tracking and running ML experiments. Considerations include:

    ●  Choosing the appropriate Google Cloud environment for development and experimentation (e.g., Vertex AI Experiments, Kubeflow Pipelines, Vertex AI TensorBoard with TensorFlow and PyTorch) given the framework

Section 3: Scaling prototypes into ML models (~18% of the exam)

3.1 Building models. Considerations include:

    ●  Choosing ML framework and model architecture

    ●  Modeling techniques given interpretability requirements

3.2 Training models. Considerations include:

    ●  Organizing training data (e.g., tabular, text, speech, images, videos) on Google Cloud (e.g., Cloud Storage, BigQuery)

    ●  Ingestion of various file types (e.g., CSV, JSON, images, Hadoop, databases) into training

    ●  Training using different SDKs (e.g., Vertex AI custom training, Kubeflow on Google Kubernetes Engine, AutoML, tabular workflows)

    ●  Using distributed training to organize reliable pipelines

    ●  Hyperparameter tuning

    ●  Troubleshooting ML model training failures

3.3 Choosing appropriate hardware for training. Considerations include:

    ●  Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)

    ●  Distributed training with TPUs and GPUs (e.g., Reduction Server on Vertex AI, Horovod)

Section 4: Serving and scaling models (~19% of the exam)

4.1 Serving models. Considerations include:

    ●  Batch and online inference (e.g., Vertex AI, Dataflow, BigQuery ML, Dataproc)

    ●  Using different frameworks (e.g., PyTorch, XGBoost) to serve models

    ●  Organizing a model registry

    ●  A/B testing different versions of a model

4.2 Scaling online model serving. Considerations include:

    ●  Vertex AI Feature Store

    ●  Vertex AI public and private endpoints

    ●  Choosing appropriate hardware (e.g., CPU, GPU, TPU, edge)

    ●  Scaling the serving backend based on the throughput (e.g., Vertex AI Prediction, containerized serving)

    ●  Tuning ML models for training and serving in production (e.g., simplification techniques, optimizing the ML solution for increased performance, latency, memory, throughput)

Section 5: Automating and orchestrating ML pipelines (~21% of the exam)

5.1 Developing end-to-end ML pipelines. Considerations include:

    ●  Data and model validation

    ●  Ensuring consistent data pre-processing between training and serving

    ●  Hosting third-party pipelines on Google Cloud (e.g., MLFlow)

    ●  Identifying components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)

    ●  Orchestration framework (e.g., Kubeflow Pipelines, Vertex AI Pipelines, Cloud Composer)

    ●  Hybrid or multicloud strategies

    ●  System design with TFX components or Kubeflow DSL (e.g., Dataflow)

5.2 Automating model retraining. Considerations include:

    ●  Determining an appropriate retraining policy

    ●  Continuous integration and continuous delivery (CI/CD) model deployment (e.g., Cloud Build, Jenkins)

5.3 Tracking and auditing metadata. Considerations include: 

    ●  Tracking and comparing model artifacts and versions (e.g., Vertex AI Experiments, Vertex ML Metadata)

    ●  Hooking into model and dataset versioning

    ●  Model and data lineage

Section 6: Monitoring ML solutions (~14% of the exam)

6.1 Identifying risks to ML solutions. Considerations include:

    ●  Building secure ML systems (e.g., protecting against unintentional exploitation of data or models, hacking)

    ●  Aligning with Google’s Responsible AI practices (e.g., biases)

    ●  Assessing ML solution readiness (e.g., data bias, fairness)

    ●  Model explainability on Vertex AI (e.g., Vertex AI Prediction)

6.2 Monitoring, testing, and troubleshooting ML solutions. Considerations include:

    ●  Establishing continuous evaluation metrics (e.g., Vertex AI Model Monitoring, Explainable AI)

    ●  Monitoring for training-serving skew

    ●  Monitoring for feature attribution drift

    ●  Monitoring model performance against baselines, simpler models, and across the time dimension

    ●  Common training and serving errors