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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/wq3grn/machine
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
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
Save this course
Save Machine Learning on AWS Deep Dive to your list so you can find it easily later:
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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 Machine Learning on AWS Deep Dive with these
activities:
Review prerequisite materials
Show steps
Review prerequisite materials such as linear algebra, calculus, and probability to strengthen your foundational knowledge and better prepare for the course.
Show steps
-
Review linear algebra concepts such as vectors, matrices, and linear transformations.
-
Refresh your understanding of calculus, including derivatives, integrals, and limits.
-
Review probability theory, including concepts such as conditional probability and Bayes' theorem.
Join a study group or online forum for machine learning
Show steps
Connect with other students and experts to discuss course topics, share knowledge, and get help with assignments.
Show steps
-
Search for study groups or online forums
-
Join a group and introduce yourself
-
Participate in discussions
-
Ask questions and help others
Explore the SageMaker Studio environment
Show steps
Take a guided tour of SageMaker Studio and learn how to navigate the user interface and use its tools for building and deploying machine learning models.
Show steps
-
Watch the guided tour videos
-
Follow along with the hands-on exercises
-
Ask questions in the discussion forum
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Complete SageMaker Studio tutorials
Show steps
Follow guided tutorials on SageMaker Studio to familiarize yourself with the platform and its capabilities for building, training, and deploying ML models.
Show steps
-
Complete the SageMaker Studio getting started tutorial.
-
Follow the tutorial on preparing data for ML.
-
Build and train an ML model using SageMaker Studio.
Build and train a machine learning model
Show steps
Build a simple model to gain a deeper understanding of the underlying concepts and to see the model creation process in practice.
Show steps
-
Collect data from a public repository
-
Clean and prepare the data
-
Select an appropriate machine learning algorithm
-
Train the model
-
Evaluate the model's performance
Attend a workshop on machine learning best practices
Show steps
Learn about best practices for building and deploying secure machine learning pipelines from industry experts.
Show steps
-
Research workshops in your area
-
Register for a workshop
-
Attend the workshop
-
Take notes and ask questions
Create a cheat sheet for ML on AWS
Show steps
Create a cheat sheet that summarizes key concepts, commands, and best practices for building, training, and deploying ML models on AWS.
Show steps
-
Gather information from course materials, documentation, and tutorials.
-
Organize the information into logical sections.
-
Create a visually appealing and easy-to-navigate cheat sheet.
Practice building and training ML models
Show steps
Practice building and training ML models on AWS to reinforce your understanding and gain hands-on experience.
Show steps
-
Create a new ML project in SageMaker Studio.
-
Upload a dataset to S3 and import it into SageMaker Studio.
-
Select an ML algorithm and configure its hyperparameters.
-
Train the ML model.
-
Evaluate the trained model and make adjustments as needed.
Contribute to an open-source machine learning project
Show steps
Gain practical experience and build your portfolio by contributing to an open-source machine learning project.
Show steps
-
Find an open-source project that aligns with your interests
-
Read the project documentation
-
Identify an area where you can contribute
-
Submit a pull request
Mentor junior ML engineers
Show steps
Mentor junior ML engineers to reinforce your own knowledge and help others develop their skills.
Show steps
-
Identify opportunities to mentor junior ML engineers.
-
Create a structured mentoring plan.
-
Provide guidance and support to your mentees.
-
Reflect on your mentoring experience and make improvements as needed.
Review prerequisite materials
Show steps
Review prerequisite materials such as linear algebra, calculus, and probability to strengthen your foundational knowledge and better prepare for the course.
Show steps
Show steps
Show steps
- Review linear algebra concepts such as vectors, matrices, and linear transformations.
- Refresh your understanding of calculus, including derivatives, integrals, and limits.
- Review probability theory, including concepts such as conditional probability and Bayes' theorem.
Join a study group or online forum for machine learning
Show steps
Connect with other students and experts to discuss course topics, share knowledge, and get help with assignments.
Show steps
Show steps
Show steps
- Search for study groups or online forums
- Join a group and introduce yourself
- Participate in discussions
- Ask questions and help others
Explore the SageMaker Studio environment
Show steps
Take a guided tour of SageMaker Studio and learn how to navigate the user interface and use its tools for building and deploying machine learning models.
Show steps
Show steps
Show steps
- Watch the guided tour videos
- Follow along with the hands-on exercises
- Ask questions in the discussion forum
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Complete SageMaker Studio tutorials
Show steps
Follow guided tutorials on SageMaker Studio to familiarize yourself with the platform and its capabilities for building, training, and deploying ML models.
Show steps
Show steps
Show steps
- Complete the SageMaker Studio getting started tutorial.
- Follow the tutorial on preparing data for ML.
- Build and train an ML model using SageMaker Studio.
Build and train a machine learning model
Show steps
Build a simple model to gain a deeper understanding of the underlying concepts and to see the model creation process in practice.
Show steps
Show steps
Show steps
- Collect data from a public repository
- Clean and prepare the data
- Select an appropriate machine learning algorithm
- Train the model
- Evaluate the model's performance
Attend a workshop on machine learning best practices
Show steps
Learn about best practices for building and deploying secure machine learning pipelines from industry experts.
Show steps
Show steps
Show steps
- Research workshops in your area
- Register for a workshop
- Attend the workshop
- Take notes and ask questions
Create a cheat sheet for ML on AWS
Show steps
Create a cheat sheet that summarizes key concepts, commands, and best practices for building, training, and deploying ML models on AWS.
Show steps
Show steps
Show steps
- Gather information from course materials, documentation, and tutorials.
- Organize the information into logical sections.
- Create a visually appealing and easy-to-navigate cheat sheet.
Practice building and training ML models
Show steps
Practice building and training ML models on AWS to reinforce your understanding and gain hands-on experience.
Show steps
Show steps
Show steps
- Create a new ML project in SageMaker Studio.
- Upload a dataset to S3 and import it into SageMaker Studio.
- Select an ML algorithm and configure its hyperparameters.
- Train the ML model.
- Evaluate the trained model and make adjustments as needed.
Contribute to an open-source machine learning project
Show steps
Gain practical experience and build your portfolio by contributing to an open-source machine learning project.
Show steps
Show steps
Show steps
- Find an open-source project that aligns with your interests
- Read the project documentation
- Identify an area where you can contribute
- Submit a pull request
Mentor junior ML engineers
Show steps
Mentor junior ML engineers to reinforce your own knowledge and help others develop their skills.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior ML engineers.
- Create a structured mentoring plan.
- Provide guidance and support to your mentees.
- Reflect on your mentoring experience and make improvements as needed.
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 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.
Data Scientist
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.
AI Engineer
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.
Cloud Architect
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.
Data Analyst
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.
Software Engineer
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.
Machine Learning Researcher
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.
Quantitative Analyst
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.
Product Manager
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.
Business Analyst
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.
Statistician
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.
Consultant
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.
Solutions Architect
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.
Data Engineer
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.
IT Manager
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.
For more career information including salaries, visit:
OpenCourser.com/course/wq3grn/machine
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.
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.
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.
Provides a comprehensive introduction to machine learning, for Java developers. It covers all the major concepts of machine learning, including data preparation, model training, and model evaluation.
Provides a comprehensive introduction to machine learning, for .NET developers. It covers all the major concepts of machine learning, including data preparation, model training, and model evaluation.
Save
Provides a comprehensive introduction to machine learning, for JavaScript developers. It covers all the major concepts of machine learning, including data preparation, model training, and model evaluation.
Provides a comprehensive introduction to machine learning, for R developers. It covers all the major concepts of machine learning, including data preparation, model training, and model evaluation.
Provides a comprehensive introduction to machine learning, using R as the programming language. It covers all the major concepts of machine learning, including data preparation, model training, and model evaluation.
Provides a comprehensive introduction to deep learning, using Python as the programming language. It covers all the major concepts of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a gentle introduction to artificial intelligence, for those who have no prior knowledge of the subject. It covers all the major concepts of AI, including machine learning, natural language processing, and computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wq3grn/machine
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Time
150 hours
Level
Graduate
Via
Pluralsight
Instructor
Amber Israelsen
Language
English
Good to know
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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