Preparing for Google Cloud Certification: Machine Learning Engineer

Prepare for the industry-recognized Google Cloud Professional Machine Learning Engineer certification. Advance your career as a machine learning engineer. Google Cloud training includes video lectures and hands-on labs using Qwiklabs.

Projects in the Google Cloud platform certification incorporate Google Cloud Platform products with concepts explained throughout the modules.

Machine Learning Engineering

Prepare for the Google Cloud Professional Machine Learning Engineer Certification Exam

ML Models

Understand how to design, build and productionalize ML models to solve business challenges using Google Cloud technologies

Certification

Understand the purpose of the Professional Machine Learning Engineer certification and its relationship to othr Google Cloud certifications

Professional Certificate Programs enable you to become empowered and successful in every phase of your job!

Dana Baker

Dana Baker, Executive Director of Regional Campuses

"We are committed to developing current and relevant coursework to help transform our next generation of leaders."

Preparing for Google Cloud Certification: Machine Learning Engineer

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Learn on your own schedule

Flexible Schedule

Set and maintain flexible deadlines

Entry Level

No previous experience required

8-Months to Complete

Suggested pace of 10 hours/week; 9 Courses

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Courses

Google Cloud Platform BigData and Machine Learning Fundamentals

This course introduces you to the big data capabilities of Google Cloud. Through a combination of presentations, demonstrations and hands-on labs, you will gain an overview of Google Cloud and a detailed understanding of the data processing and machine learning capabilities. This course showcases the ease, flexibility and power of big data solutions on Google Cloud.

How Google does Machine Learning

In this course, you will learn what machine learning is and the kinds of problems it can solve. Google’s idea of machine learning focuses on logic rather than data alone.

Ideas discussed in this course include:

  • Why logic is useful for data scientists when building a pipeline of machine learning models.
  • The five phases of converting a candidate-use case to be driven by machine learning and why it is important not to skip phases.
  • Recognition of the biases that machine learning can amplify and how to recognize this.

Launching into Machine Learning

In this course, you will study the history of machine learning and why neural networks perform well in a variety of data science problems. You will learn to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization and how to do so in a repeatable manner to support experimentation.

By the end of this course, you will be able to:

  • Identify why deep learning is currently popular.
  • Optimize and evaluate models using loss functions and performance metrics.
  • Mitigate common problems that arise in machine learning.
  • Create repeatable and scalable training, evaluation and test datasets.

Introduction to Tensorflow

This course demonstrates the flexibility and ease-of-use of TensorFlow 2.x and Keras to build, train, and deploy machine learning models.  You will learn about the TensorFlow 2.x API hierarchy and get familiar with the main components of TensorFlow through hands-on exercises.

By the end of this course, you will be able to:

  • Work with datasets and feature columns.
  • Design and build a TensorFlow 2.x input data pipeline.
  • Load csv data, numPy arrays, text data and images using tf.Data.Dataset.
  • Create numeric, categorical, bucketized and hashed feature columns.
  • Build basic linear regression, basic logistic regression and advanced logistic regression machine learning models.
  • Train, deploy and put into production machine learning models at scale with Cloud AI Platform.

Feature Engineering

This course will discuss ways to improve the accuracy of your ML models including which data columns make the most useful features. You will review good vs. bad features and how you can preprocess and transform them for optimal use in your models.

By the end of this course, you will be able to:

  • Compare the key required aspects of a good feature.
  • Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep.
  • Combine and create new feature combinations through feature crosses.
  • Understand and apply how TensorFlow transforms features.

Art and Science of Machine Learning

This course, delivered in six modules, covers the essential skills of ML intuition, good judgment and experimentation needed to finely-tune and optimize ML models for best performance.  You will learn to generalize your model using regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance.

By the end of this course, you will be able to:

  • Tune batch size and learning rate for better model performance.
  • Know the most common model optimization algorithms.
  • Optimize a ML model.
  • Apply the concepts in TensorFlow code.
  • Generalize a ML model using regularization techniques.

Production Machine Learning Systems

In this course, you will learn the components and best practices of a high-performing ML system in production environments.  It includes a review of various production ML systems: static, dynamic and continuous training; static and dynamic inference; and batch and online processing. You will explore TensorFlow abstraction levels, the various options for doing distributed training and how to write distributed training models with custom estimators.

By the end of this course, you will be able to:

  • Compare static vs. dynamic training and inference.
  • Manage model dependencies.
  • Set up distributed training for fault tolerance, replication and more.
  • Export models for portability.

ML Ops (Machine Learning Operations) Fundamentals

This course is intended for data scientists looking to quickly go from machine learning prototype to production, software engineers looking to develop machine learning engineering skills or ML engineers wanting to adopt Google Cloud for their ML production projects.

You will be introduced to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.

By the end of this course, you will be able to:

  • Identify and use core technologies required to support effective MLOps.
  • Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
  • Adopt the best CI/CD practices in the context of ML systems.
  • Implement reliable and repeatable training and inference workflows.

ML Pipelines on Google Cloud

This course is taught by Google Cloud ML engineers and trainers who work with the state-of-the-art development of ML pipelines. The first few modules will cover TensorFlow Extended (or TFX), Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata.

By the end of this course, you will be able to:

  • Recognize pipeline components and pipeline orchestration with TFX.
  • Automate your pipeline through continuous integration and continuous deployment.
  • Manage ML metadata.
  • Automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost.
  • Use Cloud Composer to orchestrate your continuous training pipelines.
  • Use MLflow for managing the complete machine learning life cycle.

Skills you will gain: