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Machine Learning on AWS Deep Dive

Amber Israelsen

In this course, *Machine Learning on AWS Deep Dive*, you’ll learn to securely build, train, and deploy machine learning (ML) projects with SageMaker Studio and other AWS technologies. First, you’ll explore the SageMaker suite of products and AWS AI/ML services. Next, you’ll discover how to use SageMaker Studio to prepare data and build, train, and deploy a machine learning model. Finally, you’ll learn how to apply security best practices to your machine learning pipelines. When you’re finished with this course, you’ll have the skills and knowledge of machine learning on AWS needed to solve real-world problems.

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Explores machine learning concepts and industry practices, which is standard in the AI/ML industry

Taught by Amber Israelsen, who are recognized for their work in AI and ML

Uses SageMaker Studio and other AWS technologies, which are highly relevant to cloud-based machine learning

Develops skills in building, training, and deploying machine learning models, which are core skills for data scientists and ML engineers

Covers security best practices for machine learning pipelines, which is crucial for protecting data and models

Requires learners to come in with some familiarity with machine learning concepts

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Career center

Learners who complete Machine Learning on AWS Deep Dive will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers design and build models to solve a variety of problems, using data to train models and algorithms. As such, a foundational understanding of how to build, train, and deploy machine learning models would help build a foundation for someone seeking to become a Machine Learning Engineer. This course provides learners with a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, potential Machine Learning Engineers can learn how to use SageMaker Studio to prepare data, build, train, and deploy machine learning models.

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Data Scientist

Data Scientists build models and algorithms to analyze data, uncover insights, and inform decision-making. To maximize their impact, many Data Scientists seek to gain experience working with advanced tools and technologies. This course provides learners with a comprehensive understanding of machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring Data Scientists can learn how to use SageMaker Studio to prepare data, build, train, and deploy machine learning models. This will help them to build a foundation for success in a competitive and rapidly evolving field.

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AI Engineer

AI Engineers design, build, and maintain AI systems. With a focus on machine learning, AI Engineers need a strong foundation in how to build, train, and deploy machine learning models. This course provides a broad overview of machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring AI Engineers can learn how to use SageMaker Studio to prepare data, build, train, and deploy machine learning models. This hands-on experience will help them grow their foundational skills.

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Cloud Architect

Cloud Architects design, build, and manage cloud computing systems. As cloud adoption continues to grow, professionals with a deep understanding of machine learning on AWS are in high demand. This course provides a comprehensive overview of machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By completing this course, aspiring or practicing Cloud Architects can solidify their understanding of machine learning and its applications. This will help them to design, build, and manage robust and scalable cloud computing systems that leverage machine learning technologies.

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Data Analyst

Data Analysts analyze and interpret data to identify trends and patterns. This course helps build a strong foundation in machine learning, covering the SageMaker suite of products and other AWS AI/ML services. Data Analysts who take this course will learn how to use SageMaker Studio to prepare data, build, train, and deploy machine learning models. This hands-on experience can strengthen their analytical skills and add valuable machine learning expertise to their resume.

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Software Engineer

Software Engineers design, develop, and test software applications. Many Software Engineers seek to specialize in a particular area, such as machine learning. This course provides a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. Software Engineers who take this course can gain valuable experience working with these tools and technologies. It will provide hands-on training in how to build, train, and deploy machine learning models, which can help them advance their career in machine learning.

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Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and techniques. A strong understanding of the latest tools and technologies is essential for success, and this course covers the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring or practicing Machine Learning Researchers can stay up-to-date on the latest advances in machine learning as applied to AWS. It provides hands-on training in how to build, train, and deploy machine learning models, which can accelerate the research pipeline.

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Quantitative Analyst

Quantitative Analysts (Quants) use mathematical and statistical models to analyze data and make investment decisions. This course provides a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring or practicing Quants can gain valuable experience working with these tools and technologies. It will provide hands-on training in how to build, train, and deploy machine learning models, opening doors to new applications and strategies in quantitative modeling and financial analysis.

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Product Manager

Product Managers are responsible for defining and managing the development of products. Many Product Managers choose to specialize in machine learning products. This course provides a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring or practicing Product Managers can gain valuable experience working with these tools and technologies. It will help them to better understand the technical side of machine learning and to make informed decisions about product development.

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Business Analyst

Business Analysts analyze business processes and systems to identify areas for improvement. By taking this course, Business Analysts can learn how to use machine learning to automate tasks and derive insights from data. This can help them to improve their analytical skills and to make more informed recommendations. The course's focus on machine learning on AWS provides exposure to the latest cloud-based tools and technologies, which can be a valuable asset for Business Analysts seeking to advance their career.

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Statistician

Statisticians collect, analyze, and interpret data to make informed decisions. This course provides a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring or practicing Statisticians can gain valuable experience working with these tools and technologies. It will provide hands-on training in how to build, train, and deploy machine learning models, which can enhance their analytical capabilities.

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Consultant

Consultants advise clients on business and technology issues. Many Consultants choose to specialize in machine learning. This course provides a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring or practicing Consultants can gain valuable experience working with these tools and technologies. It will help them to better understand the technical side of machine learning and to provide more informed advice to their clients.

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Solutions Architect

Solutions Architects design and implement technology solutions. This course provides a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring or practicing Solutions Architects can gain valuable experience working with these tools and technologies. It will help them to better understand the technical side of machine learning and to design more effective solutions for their clients.

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Data Engineer

Data Engineers design and build data pipelines to move data between systems. This course provides a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring or practicing Data Engineers can gain valuable experience working with these tools and technologies. It will help them to build more efficient and reliable data pipelines for machine learning applications.

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IT Manager

IT Managers plan, implement, and manage IT systems. Many IT Managers choose to specialize in machine learning. This course provides a deep dive into machine learning on AWS, covering the SageMaker suite of products and other AWS AI/ML services. By taking this course, aspiring or practicing IT Managers can gain valuable experience working with these tools and technologies. It will help them to better understand the technical side of machine learning and to make more informed decisions about the implementation and management of machine learning systems.

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Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning on AWS Deep Dive.

Machine Learning with PyTorch and Scikit-Learn

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Provides a comprehensive introduction to machine learning, for Python developers. It covers all the major concepts of machine learning, including data preparation, model training, and model evaluation.

Machine Learning with PyTorch and Scikit-Learn:...

Kindle Edition

Hands-On Machine Learning with Scikit-Learn, Keras,...

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Provides a practical introduction to machine learning, using Python as the programming language. It covers all the major concepts of machine learning, including data preparation, model training, and model evaluation.

Machine Learning on AWS Deep Dive

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