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    Machine Learning Engineer Learning Path

    A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems.

    school 22 activities
    update Last updated about 2 months
    person Managed by Google Cloud
    A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems. This learning path guides you through a curated collection of on-demand courses, labs, and skill badges that provide you with real-world, hands-on experience using Google Cloud technologies essential to the ML Engineer role. Once you complete the path, check out the Google Cloud Machine Learning Engineer certification to take the next steps in your professional journey.
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    01

    Professional Machine Learning Engineer Study Guide

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    access_time 2 hours
    show_chart Advanced

    This course helps learners create a study plan for the PMLE (Professional Machine Learning Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study...

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    02

    A Tour of Google Cloud Hands-on Labs

    book Lab
    access_time 45 minutes
    show_chart Introductory

    In this first hands-on lab you will access the Google Cloud console and use these basic Google Cloud features: Projects, Resources, IAM Users, Roles, Permissions, and APIs.

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    03

    Introduction to AI and Machine Learning on Google Cloud

    book Course
    access_time 8 hours
    show_chart Introductory

    This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions....

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    04

    Prepare Data for ML APIs on Google Cloud

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    access_time 6 hours 30 minutes
    show_chart Introductory

    Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and...

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    05

    Working with Notebooks in Vertex AI

    book Course
    access_time 45 minutes
    show_chart Introductory

    This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1)...

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    06

    Create ML Models with BigQuery ML

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    access_time 5 hours 30 minutes
    show_chart Intermediate

    Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in the following: creating and evaluating machine learning models with BigQuery ML to make data predictions. A skill badge is an exclusive digital badge issued by...

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    07

    Engineer Data for Predictive Modeling with BigQuery ML

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    access_time 4 hours 15 minutes
    show_chart Intermediate

    Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and...

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    08

    Feature Engineering

    book Course
    access_time 24 hours
    show_chart Introductory

    This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature...

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    09

    Build, Train and Deploy ML Models with Keras on Google Cloud

    book Course
    access_time 15 hours 30 minutes
    show_chart Intermediate

    This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.

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    10

    Production Machine Learning Systems

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    access_time 16 hours
    show_chart Intermediate

    This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training,...

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    11

    Machine Learning Operations (MLOps): Getting Started

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    access_time 8 hours
    show_chart Introductory

    This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine...

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    12

    Machine Learning Operations (MLOps) with Vertex AI: Manage Features

    book Course
    access_time 8 hours
    show_chart Intermediate

    This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Learners...

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    13

    Introduction to Generative AI

    book Course
    access_time 45 minutes
    show_chart Introductory

    This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI...

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    14

    Introduction to Large Language Models

    book Course
    access_time 30 minutes
    show_chart Introductory

    This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to...

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    15

    Machine Learning Operations (MLOps) for Generative AI

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    access_time 30 minutes
    show_chart Intermediate

    This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps...

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    16

    Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation

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    access_time 2 hours 30 minutes
    show_chart Intermediate

    This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in...

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    17

    ML Pipelines on Google Cloud

    book Course
    access_time 2 hours 15 minutes
    show_chart Advanced

    In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production...

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    18

    Build and Deploy Machine Learning Solutions on Vertex AI

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    access_time 7 hours 30 minutes
    show_chart Intermediate

    Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions with Vertex AI course, where you will learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain,...

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    19

    Create Generative AI Apps on Google Cloud

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    access_time 4 hours
    show_chart Intermediate

    Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud? In this...

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    20

    Responsible AI for Developers: Fairness & Bias

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    access_time 4 hours
    show_chart Intermediate

    This course introduces concepts of responsible AI and AI principles. It covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices. It explores practical methods and tools to implement Responsible AI best practices using Google Cloud...

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    21

    Responsible AI for Developers: Interpretability & Transparency

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    access_time 3 hours
    show_chart Intermediate

    This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.

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    22

    Responsible AI for Developers: Privacy & Safety

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    access_time 5 hours
    show_chart Intermediate

    This course introduces important topics of AI privacy and safety. It explores practical methods and tools to implement AI privacy and safety recommended practices through the use of Google Cloud products and open-source tools.

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