Machine Learning Engineer Path

Your entry point to Google Cloud's AI ecosystem. Understand the hierarchy from AI to GenAI, navigate the full portfolio of GCP AI services, and learn when to use pre-trained APIs, AutoML, or custom training. Master Vertex AI as the unified platform that ties it all together.

Data is the fuel for machine learning. This course covers the GCP data preparation ecosystem — Dataflow, Dataprep, Dataproc — and takes a deep dive into Google's pre-trained ML APIs for vision, language, speech, translation, and video intelligence. Learn when to use each tool and how to call

GCP offers multiple notebook environments — from serverless Colab Enterprise to fully customizable Vertex AI Workbench instances. This module helps you understand the trade-offs, configure runtimes, integrate with data services, and choose the right environment for every scenario on the MLE

BigQuery ML lets you create, evaluate, and predict with machine learning models using standard SQL — no data movement, no separate training infrastructure. This module covers every model type, the CREATE MODEL syntax, feature engineering, evaluation metrics, and how to export models to V

Building accurate ML models starts long before training. This module covers the full data transformation pipeline on GCP — from raw ingestion through Cloud Dataprep and Dataflow, to clean, partitioned, feature-rich tables ready for BQML. Covers exam Sections 1 and 2: data engineering and fea

Feature engineering is where domain knowledge meets data science. This module covers the complete feature engineering toolkit on GCP — from Vertex AI Feature Store for production serving, through tf.Transform for scalable preprocessing, to feature crosses, encodings, and selection techniques

This module covers the entire TensorFlow ecosystem on Google Cloud — from efficient data pipelines with tf.data to distributed training on Vertex AI. You will learn to build production-grade neural networks with Keras, optimize training with callbacks and custom loops, and scale to multi-GPU

Building a model is only the beginning — running it reliably in production is where the real engineering challenge lives. This module covers the architecture decisions, failure modes, and optimization techniques that separate prototypes from production systems: training-serving skew, distrib

Training a model is only the beginning. MLOps is the discipline of deploying, monitoring, and continuously improving ML systems in production. This module covers MLOps maturity levels, the Vertex AI MLOps toolkit, experiment tracking, model registry, metadata lineage, continuous training tri

Features are the lifeblood of ML models. The Vertex AI Feature Store provides a centralized, managed repository for storing, serving, and monitoring features — ensuring consistency between training and serving, enabling feature sharing across teams, and solving the critical problem of point-

Generative AI is reshaping how we build intelligent systems. This module covers the foundations of generative AI — what it is, how it differs from traditional ML, the models that power it, and how Google Cloud makes it accessible through Vertex AI. Essential knowledge for the GCP Machine

Large Language Models are the engines behind modern generative AI. This module goes deep into how LLMs work — from pre-training objectives and emergent capabilities to fine-tuning strategies, evaluation metrics, and inference optimization. Master the concepts needed to select, tune, and

Traditional MLOps pipelines were designed for classical ML models — tabular data, fixed feature schemas, deterministic outputs. Generative AI upends every assumption. This module covers the end-to-end lifecycle for deploying, monitoring, and iterating on foundation models in production using

Model evaluation is the gatekeeper between development and production. A model that scores well on training data can fail catastrophically in the real world. This module covers every metric, method, and tool you need for evaluating both classical ML and generative AI models on GCP — from con

Production ML is not a single model — it is an orchestrated system of data ingestion, validation, transformation, training, evaluation, and deployment. This module covers TFX, Kubeflow Pipelines, Vertex AI Pipelines, and Cloud Composer so you can build reproducible, auditable, and fully auto

From AutoML to custom training, from online endpoints to batch prediction — this module covers the full lifecycle of building, deploying, and monitoring ML models on Vertex AI. Learn when to use AutoML versus custom containers, how to configure endpoints for production traffic, and how to se

From prompt engineering to RAG pipelines, function calling, and multimodal apps — this module covers the full spectrum of building production generative AI applications on Google Cloud using Vertex AI, Agent Builder, and Gemini. Master the patterns that appear repeatedly on the MLE exam.

Building ML systems that are fair, unbiased, and accountable is not optional — it is a core competency tested on the MLE exam. This module covers bias detection, fairness metrics, mitigation strategies, and Google's responsible AI tooling on Vertex AI.

A model that cannot be explained cannot be trusted. This module covers the full spectrum of interpretability techniques — from intrinsic methods like linear coefficients and decision-tree splits to post-hoc explainers like SHAP, LIME, and Partial Dependence Plots. You will learn how to use V

Privacy and safety are the guardrails that make AI deployment viable in regulated industries and consumer-facing applications. This module covers the full stack: PII detection with Cloud DLP, differential privacy with TensorFlow Privacy, federated learning, encryption with Cloud KMS, AI