Preparing for Google Cloud Certification: Machine Learning Engineer

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Preparing for Google Cloud Certification: Machine Learning Engineer

Author: Google Cloud

87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support

  1. Complete the Pluralsight Machine Learning on Google Cloud Certification Path
  2. Review other recommended resources for the Google Cloud Professional Machine Learning Engineer exam
  3. Review the Professional Machine Learning Engineer exam guide
  4. Complete the Professional Machine Learning Engineer sample questions
  5. Register for the Google Cloud certification exam (remotely or at a test center)
... Read more

  1. Learn the skills needed to be successful in a machine learning engineering role.
  2. Prepare for the Google Cloud Professional Machine Learning Engineer certification exam.
  3. Understand how to design, build, productionalize ML models to solve business challenges using Google Cloud technologies.
  4. Understand the purpose and intent of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications.

Pre-requisites

3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud.

Preparing for Google Cloud Machine Learning Engineer Certification

Advance your career as an Machine Learning Engineer

Google Cloud Platform Big Data and Machine Learning Fundamentals

by Google Cloud

Dec 17, 2020 / 4h 55m

4h 55m

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Description

This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud.

Table of contents
  1. Introduction to the Data and Machine Learning on Google Cloud Course
  2. Introduction to Google Cloud Platform
  3. Recommending Products using Cloud SQL and Spark
  4. Predict Visitor Purchases Using BigQuery ML
  5. Real-time IoT Dashboards with Pub/Sub, Dataflow, and Data Studio
  6. Deriving Insights from Unstructured Data using Machine Learning
  7. Summary

How Google does Machine Learning

by Google Cloud

Dec 10, 2020 / 3h 13m

3h 13m

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Description

What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

Table of contents
  1. Introduction to Course
  2. What it means to be AI first
  3. How Google does ML
  4. Inclusive ML
  5. Python Notebooks in the Cloud
  6. Summary

Launching into Machine Learning

by Google Cloud

Nov 11, 2020 / 3h 53m

3h 53m

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Description

Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way so as to support experimentation.

Table of contents
  1. Introduction to Course
  2. Improve Data Quality and Exploratory Data Analysis
  3. Practical ML
  4. Optimization
  5. Generalization and Sampling
  6. Summary
  7. Course Resources

Introduction to TensorFlow

by Google Cloud

Aug 13, 2020 / 1h 59m

1h 59m

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Description

This course is focused on using 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 will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns. We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.

Table of contents
  1. Introduction to course
  2. Introduction to TensorFlow
  3. Design and Build a TensorFlow Input Data Pipeline
  4. Training neural networks with Tensorflow 2 and the Keras Sequential API
  5. Training neural networks with Tensorflow 2 and Keras Functional API
  6. Summary
  7. Course Resource

Feature Engineering

by Google Cloud

Sep 28, 2020 / 3h 2m

3h 2m

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Description

Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering where we will discuss good vs bad features and how you can preprocess and transform them for optimal use in your models.

Table of contents
  1. Introduction to Course
  2. Raw Data to Features
  3. Preprocessing and feature creation
  4. Feature Crosses
  5. TensorFlow Transform
  6. Summary
  7. Course Resources

Art and Science of Machine Learning

by Google Cloud

Dec 17, 2020 / 2h 39m

2h 39m

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Description

Welcome to the Art and Science of Machine Learning. This course is delivered in 6 modules. The course covers the essential skills of ML intuition, good judgment and experimentation needed to finely tune and optimize ML models for the best performance. You will learn how to generalize your model using Regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code.

Table of contents
  1. Introduction
  2. The Art of ML
  3. Hyperparameter Tuning
  4. A Pinch of Science
  5. The Science of Neural Networks
  6. Embeddings
  7. Summary

Production Machine Learning Systems

by Google Cloud

Sep 9, 2021 / 2h 42m

2h 42m

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Description

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, and how to write distributed training models with custom estimators.

Table of contents
  1. Introduction to Advanced Machine Learning on Google Cloud
  2. Architecting Production ML Systems
  3. Designing Adaptable ML Systems
  4. Designing High-Performance ML Systems
  5. Building Hybrid ML Systems
  6. Summary

MLOps (Machine Learning Operations) Fundamentals

by Google Cloud

May 10, 2021 / 5h 24m

5h 24m

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Description

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 Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.

Table of contents
  1. Why and When do we need MLOps
  2. Understanding the main Kubernetes components (Optional)
  3. Introduction to AI Platform Pipelines
  4. Training, Tuning and Serving on AI Platform
  5. Kubeflow Pipelines on AI Platform
  6. CI/CD for Kubeflow Pipelines on AI Platform
  7. Course Summary

ML Pipelines on Google Cloud

by Google Cloud

Feb 18, 2021 / 3h 4m

3h 4m

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Description

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 machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.

Table of contents
  1. Introduction
  2. Introduction to TFX Pipelines
  3. Pipeline orchestration with TFX
  4. Custom components and CI/CD for TFX pipelines
  5. ML Metadata with TFX
  6. Continuous Training with multiple SDKs, KubeFlow & AI Platform Pipelines
  7. Continuous Training with Cloud Composer
  8. ML Pipelines with MLflow
  9. Summary
Learning Paths

Preparing for Google Cloud Certification: Machine Learning Engineer

  • Number of Courses9 courses
  • Duration31 hours

87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification.

Here's what you have to do

  1. Complete the Pluralsight Machine Learning on Google Cloud Certification Path
  2. Review other recommended resources for the Google Cloud Professional Machine Learning Engineer exam
  3. Review the Professional Machine Learning Engineer exam guide
  4. Complete the Professional Machine Learning Engineer sample questions
  5. Register for the Google Cloud certification exam (remotely or at a test center)

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