Designing and Implementing Solutions Using Google Cloud AutoML from Pluralsight

Google Cloud AI offers a wide range of machine learning services. AutoML features cutting-edge technology which uses your training data to find the best model for your use case. In this course, you'll learn to build a custom machine learning model.

Most organizations want to harness the power of machine learning in order to improve their products, but they may not always have the expertise available in-house. In this course, Designing and Implementing Solutions Using Google Cloud AutoML, you’ll learn how you can train custom machine learning models on your dataset with just a few clicks on the UI or a few commands on a terminal window. This course will also show how engineers and analysts can harness the power of ML for common use cases by using AutoML to build their own model, trained on their own data, without needing any specific machine learning expertise.

First, you'll see an overview of the suite of machine learning services available on the Google Cloud and understand the features of each so you can make the right choice of service for your use case. You’ll learn about the basic concepts underlying AutoML which uses neural architecture search and transfer learning to find the best neural network for your custom use case.

Next, you'll explore AutoML’s translation model, and feed in sentence pairs to the TMX format to perform German-English translation. You’ll use your custom model for prediction from the UI, from the command line, and by using Python APIs. You’ll also learn to understand the significance of the BLEU score to analyze the quality of your translation model.

Finally, you'll use the natural language APIs that AutoML offers to build a model for sentiment analysis of reviews and work with AutoML for image classification using the AutoML Vision APIs. You'll finish up by learning the basic requirements of the data needed to train this model and develop a classifier that can identify fruits.

At the end of this course, you will be very comfortable choosing the right ML API that fits your use case and using AutoML to build complex neural networks trained on your own dataset for common problems.

What's inside

Syllabus

Course Overview

Introducing Google Cloud AutoML

Performing Custom Translation Using AutoML Translation

Working with Language Using AutoML Natural Language

Working with Images Using AutoML Vision

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches learners how to train their own machine learning models by using AutoML

Focuses on training custom machine learning models on specific datasets with just a few clicks on the UI or a few commands on a terminal window, making it accessible to engineers and analysts with limited machine learning expertise

Provides hands-on experience with building and deploying machine learning models using AutoML's translation, natural language, and image classification APIs

Covers the basic concepts underlying AutoML, including neural architecture search and transfer learning, enabling learners to understand the mechanics behind the model training process

Develops expertise in choosing the right Google Cloud AI ML API for specific use cases, empowering learners to make informed decisions about the best tool for their needs

Provides an overview of the suite of machine learning services available on the Google Cloud, helping learners understand the broader landscape of AI solutions

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Designing and Implementing Solutions Using Google Cloud AutoML with these activities:

Review basic concepts of machine learning

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Establish a solid foundation for this course by reviewing the fundamental concepts of machine learning.

Browse courses on Machine Learning

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Read articles or watch videos on supervised learning algorithms.
Review the different types of neural networks and how they work.
Practice implementing basic machine learning algorithms.

Organize and review notes and course materials

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Stay organized and ensure comprehension by regularly compiling and reviewing notes and other course materials.

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After each lecture, summarize the key takeaways and add them to your notes.
Review your notes before each new lecture.
Organize your notes and materials using a clear and logical system.

Participate in a peer study group

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Enhance understanding and foster a sense of community by engaging in discussions with fellow students through peer study groups.

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Find a group of classmates who are also enthusiastic about learning about Google Cloud AutoML.
Meet regularly to discuss course concepts, work on assignments together, and quiz each other.

Four other activities

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Tutorial: Use AutoML Vision to classify fruit images

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Gain hands-on experience with AutoML Vision by following a guided tutorial on classifying fruit images.

Browse courses on Image Classification

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Sign up for a free AutoML Vision trial.
Follow the tutorial's instructions to create a dataset and upload fruit images.
Create an AutoML Vision model and select 'Image classification' as the task.
Train the model and monitor its progress.
Evaluate the model's accuracy on a test set of fruit images.

Build a German-English translation model on custom data

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Practice building custom machine learning models with AutoML Translation by developing a German-English translation model.

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Prepare a dataset of sentence pairs in German and English.
Create a new AutoML Translation model and select 'German' as the source language and 'English' as the target language.
Import the dataset into the model.
Train the model and monitor its progress.
Evaluate the model's performance using the BLEU score.

Project: Build a sentiment analysis model for customer reviews

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Develop a real-world application by building a sentiment analysis model for customer reviews using AutoML Natural Language.

Browse courses on Sentiment Analysis

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Collect a dataset of customer reviews with corresponding sentiment labels.
Create a new AutoML Natural Language model and select 'Sentiment analysis' as the task.
Import the dataset into the model.
Train the model and monitor its progress.
Deploy the model and integrate it into a web application or other platform.

Attend workshops on advanced topics in AutoML

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Expand your knowledge and skills by attending workshops that focus on advanced topics related to Google Cloud AutoML.

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Research and find workshops that align with your interests and learning goals.
Register for the workshops and actively participate in the sessions.

Career center

Learners who complete Designing and Implementing Solutions Using Google Cloud AutoML will develop knowledge and skills that may be useful to these careers:

Software Engineer

Software Engineers primarily design and develop computer programs and applications. This course would be useful for those who wish to expand their current knowledge of working with AutoML.

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Data Scientist

Data Scientists use a combination of coding skills, business knowledge, and math to create predictive models for their company's use. Usually using a variety of different algorithms and cloud platforms, Data Scientists may use the skills learned from this course to enhance their already existing knowledge of AutoML. This course may also be useful for those transitioning into a new career field.

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Cloud Architect

Cloud Architects design, build, and manage scalable, reliable, and secure cloud computing solutions. This course could help those looking to specialize in Google's AutoML.

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Machine Learning Engineer

Machine Learning Engineers work on the practical aspects of implementing machine learning solutions. This course may be useful for those looking to transition into this field or those looking to enhance their understanding of Google's AutoML.

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Data Analyst

Data Analysts are responsible for making data-driven decisions. This course may be useful for those interested in enhancing their knowledge of AutoML.

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Database Administrator

Database Administrators manage and maintain the databases that store an organization's data. This course may be useful for those who wish to enhance their knowledge of Google's AutoML.

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Information Security Analyst

Information Security Analysts design and implement security measures to protect an organization's computer networks and systems. This course may be useful to those who wish to grow their knowledge of Google's AutoML.

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IT Manager

IT Managers are responsible for the overall management of an organization's IT systems and services. This course could help enhance their understanding of the uses of AutoML.

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Project Manager

Project Managers plan, organize, and execute projects. This course could help those interested in working with AutoML to enhance their knowledge.

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Business Analyst

Business Analysts analyze business processes and propose solutions to improve efficiency or productivity. This course may be useful for those looking to become familiar with AutoML.

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Help Desk Analyst

Help Desk Analysts provide technical support to users of computer systems or software. This course could be useful to learn about Google's AutoML.

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Quality Assurance Analyst

Quality Assurance Analysts ensure that software and other products meet quality standards. This course may be useful for those who want to enhance their working knowledge of Google's AutoML.

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Technical Support Specialist

Technical Support Specialists provide technical support and assistance to users of computer systems or software. This course could be useful to learn about Google's AutoML.

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Network Administrator

Network Administrators manage and maintain computer networks. This course may be useful to gain a foundational understanding of Google's AutoML.

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System Administrator

System Administrators manage and maintain computer systems and networks. This course could be useful to gain a foundational understanding of Google's AutoML.

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