Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker from Pluralsight

This course is an in-depth introduction to SageMaker and the support it offers to train and deploy machine learning models in a distributed environment.

SageMaker is a fully managed machine learning (ML) platform on AWS which makes prototyping, building, training, and hosting ML models very simple indeed. In this course, Deep Learning Using TensorFlow and Apache MXNet on Amazon SageMaker, you'll be shown how to use the built-in algorithms, such as the linear learner and PCA, hosted on SageMaker containers. The only code you need to write is to prepare your data. You'll then see the 3 different ways in which you build your own custom model on SageMaker. You'll bring your own pre-trained model and host it on SageMaker's first party containers. You'll then work on building your model using Apache MXNet and finally bring a custom container to be trained on SageMaker. When you have finished with this course, you will also know how you can connect to other AWS services such as S3 and Redshift to access your training data, run training in a distributed manner, and autoscale your model variants.

What's inside

Syllabus

Course Overview

Machine Learning on the Cloud with AWS SageMaker

Using Built-in Algorithms in SageMaker

Using Custom Code, Models, and Containers in SageMaker

Implementing Distributed Training and Autoscaling on SageMaker

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches how to prepare data and use AWS provided container to host algorithm with minimal coding in your part

Provides an in-depth understanding of SageMaker, its features, and its applications

Covers a range of topics, from building custom models to autoscaling, providing a comprehensive overview of SageMaker

Taught by Janani Ravi, an experienced instructor in machine learning and deep learning

Requires some prior knowledge of machine learning and deep learning concepts

Focuses on Amazon's SageMaker platform, which may limit the applicability of the skills learned to other cloud platforms

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 Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker with these activities:

Read Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido

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Familiarize yourself with the fundamentals of machine learning and Python.

View Introduction to Machine Learning with Python: A... on Amazon

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Read the first three chapters of the book.
Work through the exercises at the end of each chapter.

Complete the TensorFlow tutorials

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Develop hands-on experience with TensorFlow.

Browse courses on TensorFlow

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Follow the TensorFlow tutorials on the official website.
Experiment with the code samples provided in the tutorials.

Watch the Coursera course on Machine Learning by Andrew Ng

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Gain a comprehensive understanding of machine learning concepts.

Browse courses on Machine Learning

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Enroll in the Coursera course on Machine Learning.
Watch the video lectures and complete the assignments.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Join a study group or online forum for SageMaker

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Connect with other learners and experts to enhance your understanding.

Browse courses on SageMaker

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Identify and join a relevant study group or online forum.
Participate in discussions and ask questions.

Build a machine learning model using SageMaker

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Apply your knowledge of SageMaker to a practical project.

Browse courses on SageMaker

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Choose a dataset and a machine learning algorithm.
Create a SageMaker notebook instance.
Train and deploy your model using SageMaker.

Participate in a Kaggle competition using SageMaker

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Challenge yourself and test your skills in a real-world setting.

Browse courses on SageMaker

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Choose a Kaggle competition that aligns with your interests.
Build a SageMaker model and submit it to the competition.

Mentor a junior learner or colleague on SageMaker

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Solidify your understanding by teaching others.

Browse courses on SageMaker

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Identify a junior learner or colleague who could benefit from your guidance.
Provide support and answer their questions on SageMaker.

Career center

Learners who complete Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker will develop knowledge and skills that may be useful to these careers:

Data Scientist

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Data Scientist. This in-depth introduction to SageMaker and all that it can do to help you as you train and deploy machine learning models in a distributed environment can help you build the foundation necessary to succeed as a Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Machine Learning Engineer. The information you learn here, regarding how to use the built-in algorithms and hosted containers on SageMaker can help you perform your duties as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Cloud Architect

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Cloud Architect. This in-depth introduction to SageMaker and all that it can do to help you as you train and deploy machine learning models in a distributed environment can help you build the foundation necessary to succeed as a Cloud Architect.

See salaries and explore the career path for Cloud Architect

Software Engineer

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Software Engineer. This in-depth introduction to Amazon SageMaker and its role in training and deploying machine learning models in a distributed environment can give you an edge over other candidates, especially since you'll know how to use the built-in algorithms, such as the linear learner and PCA, hosted on SageMaker containers.

See salaries and explore the career path for Software Engineer

Data Engineer

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Data Engineer. The information you will learn in this course is valuable because it gives you a strong foundation in all that SageMaker has to offer. You will learn how to prepare your data, train your models, and deploy them using SageMaker. This will give you a competitive edge in the job market.

See salaries and explore the career path for Data Engineer

Machine Learning Specialist

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Machine Learning Specialist. The knowledge and skills you gain from this course can help build a strong foundation for your career, helping you to succeed as a Machine Learning Specialist.

See salaries and explore the career path for Machine Learning Specialist

Data Analyst

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Data Analyst. The knowledge and skills you gain from this course can help build a strong foundation for your career, helping you to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Artificial Intelligence Engineer

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as an Artificial Intelligence Engineer. The knowledge and skills you gain from this course, including the development of your deep learning skills, can help build a strong foundation for your career as an Artificial Intelligence Engineer.

See salaries and explore the career path for Artificial Intelligence Engineer

Cloud Developer

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Cloud Developer. SageMaker is a fully managed machine learning (ML) platform on AWS which makes prototyping, building, training, and hosting ML models very simple indeed. This course can help you become more efficient in your role as a Cloud Developer.

See salaries and explore the career path for Cloud Developer

Research Scientist

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Research Scientist. The information you learn here, regarding how to use the built-in algorithms and hosted containers on SageMaker can help you perform your duties as a Research Scientist.

See salaries and explore the career path for Research Scientist

Software Developer

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Software Developer. This course can help you become familiar with SageMaker, a fully managed machine learning (ML) platform on AWS which makes prototyping, building, training, and hosting ML models very simple indeed. With this knowledge, you can be more effective as a Software Developer.

See salaries and explore the career path for Software Developer

Data Science Manager

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Data Science Manager. This course can help you become familiar with all that SageMaker can do, which can help make you a more effective manager.

See salaries and explore the career path for Data Science Manager

Business Analyst

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Business Analyst. The knowledge and skills you gain from this course can help build a strong foundation for your career, helping you to succeed as a Business Analyst.

See salaries and explore the career path for Business Analyst

Product Manager

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Product Manager. The information you learn here, regarding how to use the built-in algorithms and hosted containers on SageMaker can help you perform your duties as a Product Manager.

See salaries and explore the career path for Product Manager

Systems Analyst

This course, Deep Learning Using TensorFlow and Apache MXNet on Amazon Sagemaker, may be useful for someone seeking a career as a Systems Analyst. The information you learn here, regarding how to use the built-in algorithms and hosted containers on SageMaker can help you perform your duties as a Systems Analyst.

See salaries and explore the career path for Systems Analyst