Deploying TensorFlow Models to AWS, Azure, and the GCP from Pluralsight

This course will help the data scientist or engineer with a great ML model, built in TensorFlow, deploy that model to production locally or on the three major cloud platforms; Azure, AWS, or the GCP.

Deploying and hosting your trained TensorFlow model locally or on your cloud platform of choice - Azure, AWS or, the GCP, can be challenging. In this course, Deploying TensorFlow Models to AWS, Azure, and the GCP, you will learn how to take your model to production on the platform of your choice. This course starts off by focusing on how you can save the model parameters of a trained model using the Saved Model interface, a universal interface for TensorFlow models. You will then learn how to scale the locally hosted model by packaging all dependencies in a Docker container. You will then get introduced to the AWS SageMaker service, the fully managed ML service offered by Amazon. Finally, you will get to work on deploying your model on the Google Cloud Platform using the Cloud ML Engine. At the end of the course, you will be familiar with how a production-ready TensorFlow model is set up as well as how to build and train your models end to end on your local machine and on the three major cloud platforms. Software required: TensorFlow, Python.

What's inside

Syllabus

Course Overview

Using TensorFlow Serving

Containerizing TensorFlow Models Using Docker on Microsoft Azure

Deploying TensorFlow Models on Amazon AWS

Deploying TensorFlow Models on the Google Cloud Platform

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

The course focuses on practical implementation, which is standard in industry use cases

Taught by Janani Ravi, who is a recognized expert in deploying machine learning models on cloud platforms

Develops skills in deploying TensorFlow models on local and cloud platforms, which are core skills for data scientists and engineers

Students explicitly expected to have background knowledge of TensorFlow and Python

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 Deploying TensorFlow Models to AWS, Azure, and the GCP with these activities:

Organize and review course materials

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Enhance your learning experience by organizing and regularly reviewing the course notes, assignments, and materials.

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Create a system for organizing your notes, assignments, and other course materials.
Dedicate time to reviewing and summarizing the key concepts covered in each class.

Review data science and machine learning basics

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Brush up on the foundational concepts of data science and machine learning to strengthen your understanding of TensorFlow.

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Review fundamental concepts like data types, data structures, and algorithms.
Revise key machine learning algorithms such as linear regression, decision trees, and k-nearest neighbors.

Participate in a study group for TensorFlow model deployment

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Improve your grasp of TensorFlow model deployment by engaging in discussions and collaborative problem-solving with peers.

Browse courses on TensorFlow

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Join or form a study group with classmates or online learners.
Schedule regular meetings to discuss course concepts, share knowledge, and work through challenges collectively.

Five other activities

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Follow tutorials on TensorFlow Serving

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Enhance your understanding of TensorFlow Serving by completing guided tutorials that demonstrate its features and usage.

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Explore the official TensorFlow Serving documentation for detailed guidance.
Utilize online tutorials and resources to practice implementing TensorFlow Serving in your projects.

Solve practice questions on TensorFlow model deployment

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Enhance your understanding of TensorFlow model deployment by attempting practice questions and evaluating your solutions.

Browse courses on TensorFlow

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Find online or textbook-based practice questions that cover TensorFlow model deployment topics.
Attempt to solve the questions, referring back to course materials or seeking additional resources if needed.

Develop a Docker container for TensorFlow models

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Solidify your comprehension by creating a Docker container to package and deploy TensorFlow models, enhancing your skills in containerization.

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Create a Dockerfile that defines the environment and dependencies for your TensorFlow model.
Build and test the Docker image to ensure it runs your model correctly.

Practice deploying TensorFlow models on AWS

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Reinforce your knowledge of deploying TensorFlow models on AWS through hands-on practice.

Browse courses on TensorFlow

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Set up an AWS account and configure your environment for TensorFlow.
Use AWS SageMaker to deploy your TensorFlow model and monitor its performance.

Contribute to open-source TensorFlow projects

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Deepen your understanding and contribute to the TensorFlow community by actively participating in open-source projects.

Browse courses on TensorFlow

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Identify open-source TensorFlow projects that align with your interests.
Contribute to code development, documentation, or testing of these projects.

Career center

Learners who complete Deploying TensorFlow Models to AWS, Azure, and the GCP will develop knowledge and skills that may be useful to these careers:

Data Scientist

Your training as a Data Scientist has almost certainly prepared you to make the most of this course. In your day-to-day activities, you might find yourself working closely with DevOps teams, as well as front-end and back-end engineers. This course will help you optimize communication and collaboration across disciplines. You'll also be able to more autonomously perform model deployment and hosting tasks, which will free up your engineering teammates to focus on their projects.

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Machine Learning Engineer

As a Machine Learning Engineer, you'll likely be quite familiar with deploying models, but this course may still prove helpful by providing a comprehensive technical review, as well as training you on how to perform these tasks on the AWS, Azure, and GCP platforms. Acquiring these cloud-based skills will make you a more desirable candidate for openings at the larger tech firms.

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Cloud Engineer

You'll learn how to deploy and host models using various cloud services in this course, including AWS SageMaker and Google Cloud ML Engine. Doing so will help you attain a better understanding of how to optimize machine learning model performance, allowing you to better support data scientists on your team.

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DevOps Engineer

As a DevOps Engineer, you'll likely be working with data scientists to deploy and maintain their models. This course will give you an introduction to how this is done, which you can then use to improve your ability to align with the needs of your teammates.

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Data Analyst

Data Analysts are commonly required to become generalists over the course of their careers. This is particularly evident in the case of those working in a cloud-based environment. This training will provide you with a foundation for deploying models consisting of TensorFlow on the major cloud platforms. Doing so will prove advantageous as your career progresses.

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Software Engineer

Software Engineers may find this course useful for gaining a better understanding of how to deploy models using the TensorFlow Serving API. Furthermore, taking it may make you more attractive to employers in the tech sector.

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Systems Engineer

This course may be useful for Systems Engineers seeking to develop a more robust understanding of how to deploy TensorFlow models on various cloud platforms.

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Network Engineer

Network Engineers who work in cloud environments may find this course helpful for improving their knowledge of deploying TensorFlow models on major cloud platforms.

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Database Administrator

Some Database Administrators are tasked with managing databases used for machine learning. For those who are, this course may prove useful for gaining a better understanding of how models consisting of TensorFlow are deployed.

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Business Analyst

While not strictly necessary, this course may prove useful for Business Analysts who want to broaden their knowledge of machine learning model deployment.

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Product Manager

For Product Managers who intend to move into the tech industry, this course may prove helpful for acquiring foundational knowledge in deploying machine learning models.

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Technical Writer

Technical Writers who specialize in cloud-based technologies may find this course helpful for gaining a better understanding of how to deploy TensorFlow models on various cloud platforms.

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IT Manager

IT Managers who are responsible for overseeing machine learning teams may find this course helpful for gaining a better understanding of how TensorFlow models are deployed.

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Project Manager

Project Managers in the tech field may find this course helpful for gaining a better understanding of how machine learning models are deployed.

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Sales Engineer

Sales Engineers selling cloud-based solutions may find this course helpful for gaining a better understanding of how TensorFlow models are deployed.

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