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Introduction to Amazon SageMaker Neo
AWS
AWS
Amazon SageMaker Neo is a new machine learning capability that enables developers to deploy machine learning models from multiple frameworks to multiple platforms in the cloud and at the edge.
Read more
Amazon SageMaker Neo is a new machine learning capability that enables developers to deploy machine learning models from multiple frameworks to multiple platforms in the cloud and at the edge.
Read more
Amazon SageMaker Neo is a new machine learning capability that enables developers to deploy machine learning models from multiple frameworks to multiple platforms in the cloud and at the edge.
Amazon SageMaker Neo is a new machine learning capability that enables developers to deploy machine learning models from multiple frameworks to multiple platforms in the cloud and at the edge. In this course we cover how Neo does this by converting a model from its framework-specific format into portable code that can run on multiple platforms. During conversion, Neo automatically optimizes machine learning models to one-hundredth of their original size with twice the performance and no loss in accuracy. Also covered are the benefits and key features of the service, along with some possible use cases for Neo.
Register for this course and see more details by visiting:
OpenCourser.com/course/6qc741/introduction
What's inside
Syllabus
Introduction to Amazon SageMaker Neo
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Excellent course for developers who want to learn how to deploy their own ML models to multiple platforms
Taught by AWS, the leaders in cloud computing
Provides a comprehensive overview of Amazon SageMaker Neo
Covers advanced topics such as model optimization and performance tuning
Includes hands-on exercises to reinforce learning
Save this course
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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 Introduction to Amazon SageMaker Neo with these
activities:
Review AWS Fundamentals
Show steps
Reinforce your understanding of AWS basics, including core services, infrastructure, and best practices.
Show steps
-
Revise AWS whitepapers and documentation
-
Complete hands-on labs or tutorials on AWS services
Review Python language fundamentals
Show steps
This activity will help you refresh your foundational knowledge of Python, ensuring you have a solid base to build on throughout the course.
Browse courses on
Python
Show steps
-
Review Python syntax and data types
-
Practice writing simple Python programs
Create a blog post on Amazon SageMaker Neo
Show steps
Enhance your understanding of Amazon SageMaker Neo by writing a blog post that shares your knowledge with others.
Show steps
-
Research Amazon SageMaker Neo and its features
-
Write a draft of the blog post
-
Edit and refine the blog post
-
Publish the blog post on a relevant platform
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group focused on Amazon SageMaker Neo
Show steps
Connect with peers and engage in discussions to reinforce the concepts learned in the course.
Show steps
-
Find or create a study group for Amazon SageMaker Neo
-
Attend regular study sessions
-
Participate in discussions and share knowledge
Develop machine learning models
Show steps
Practice applying the concepts learned about framework conversion and optimization to develop practical machine learning models.
Browse courses on
Machine Learning Development
Show steps
-
Select a dataset and define the problem to be solved
-
Choose a machine learning algorithm and train the model
-
Validate the model and adjust parameters as necessary
-
Deploy the model and evaluate its performance
Build a web application using Amazon SageMaker Neo
Show steps
Gain hands-on experience deploying machine learning models in web applications.
Show steps
-
Set up the web application framework and environment
-
Integrate Amazon SageMaker Neo with the application
-
Deploy the web application and test its functionality
Follow tutorials on deploying machine learning models with Neo
Show steps
This activity will provide practical guidance on how to deploy your machine learning models using Neo, complementing the theoretical concepts covered in the course.
Browse courses on
Machine Learning Deployment
Show steps
-
Find tutorials on deploying machine learning models with Neo
-
Follow the tutorials step-by-step
-
Experiment with different deployment options and use cases
Build a machine learning project using Amazon SageMaker Neo
Show steps
This activity will allow you to apply your knowledge in a practical setting, building a machine learning project that leverages Neo's capabilities.
Browse courses on
Machine Learning Projects
Show steps
-
Define the project scope and objectives
-
Gather and prepare your data
-
Train and evaluate your machine learning model
-
Deploy your model using Amazon SageMaker Neo
Create a blog post or video tutorial on a specific Neo feature
Show steps
This activity will encourage you to delve deeper into Neo's features and apply your knowledge by creating content that can benefit others.
Browse courses on
Machine Learning Applications
Show steps
-
Choose a specific Neo feature to focus on
-
Research and gather information on the feature
-
Create a blog post or video tutorial explaining the feature and its applications
Review AWS Fundamentals
Show steps
Reinforce your understanding of AWS basics, including core services, infrastructure, and best practices.
Show steps
Show steps
Show steps
- Revise AWS whitepapers and documentation
- Complete hands-on labs or tutorials on AWS services
Review Python language fundamentals
Show steps
This activity will help you refresh your foundational knowledge of Python, ensuring you have a solid base to build on throughout the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax and data types
- Practice writing simple Python programs
Create a blog post on Amazon SageMaker Neo
Show steps
Enhance your understanding of Amazon SageMaker Neo by writing a blog post that shares your knowledge with others.
Show steps
Show steps
Show steps
- Research Amazon SageMaker Neo and its features
- Write a draft of the blog post
- Edit and refine the blog post
- Publish the blog post on a relevant platform
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group focused on Amazon SageMaker Neo
Show steps
Connect with peers and engage in discussions to reinforce the concepts learned in the course.
Show steps
Show steps
Show steps
- Find or create a study group for Amazon SageMaker Neo
- Attend regular study sessions
- Participate in discussions and share knowledge
Develop machine learning models
Show steps
Practice applying the concepts learned about framework conversion and optimization to develop practical machine learning models.
Browse courses on
Machine Learning Development
Show steps
Show steps
Show steps
- Select a dataset and define the problem to be solved
- Choose a machine learning algorithm and train the model
- Validate the model and adjust parameters as necessary
- Deploy the model and evaluate its performance
Build a web application using Amazon SageMaker Neo
Show steps
Gain hands-on experience deploying machine learning models in web applications.
Show steps
Show steps
Show steps
- Set up the web application framework and environment
- Integrate Amazon SageMaker Neo with the application
- Deploy the web application and test its functionality
Follow tutorials on deploying machine learning models with Neo
Show steps
This activity will provide practical guidance on how to deploy your machine learning models using Neo, complementing the theoretical concepts covered in the course.
Browse courses on
Machine Learning Deployment
Show steps
Show steps
Show steps
- Find tutorials on deploying machine learning models with Neo
- Follow the tutorials step-by-step
- Experiment with different deployment options and use cases
Build a machine learning project using Amazon SageMaker Neo
Show steps
This activity will allow you to apply your knowledge in a practical setting, building a machine learning project that leverages Neo's capabilities.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Define the project scope and objectives
- Gather and prepare your data
- Train and evaluate your machine learning model
- Deploy your model using Amazon SageMaker Neo
Create a blog post or video tutorial on a specific Neo feature
Show steps
This activity will encourage you to delve deeper into Neo's features and apply your knowledge by creating content that can benefit others.
Browse courses on
Machine Learning Applications
Show steps
Show steps
Show steps
- Choose a specific Neo feature to focus on
- Research and gather information on the feature
- Create a blog post or video tutorial explaining the feature and its applications
Career center
Learners who complete Introduction to Amazon SageMaker Neo will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for the complete machine learning lifecycle, from gathering data to deploying and monitoring models. They collaborate with domain experts and data scientists to understand business objectives and translate them into technical requirements. Amazon SageMaker Neo is a powerful tool that can help Machine Learning Engineers deploy high-quality models with speed and efficiency. Engineers can take models from various frameworks and deploy them on various platforms, including the cloud and the edge, without sacrificing accuracy or performance. By learning about the capabilities and benefits of Amazon SageMaker Neo, Machine Learning Engineers can optimize their model deployment process and deliver exceptional results for their organizations.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software systems. They work in various industries, including technology, finance, healthcare, and manufacturing. Amazon SageMaker Neo can be a valuable tool for Software Engineers involved in deploying machine learning models as part of their software systems. By using Neo, Software Engineers can simplify the model deployment process and ensure that their models are running efficiently and reliably. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Software Engineers who want to enhance their skills in this area.
Data Scientist
Data Scientist
Data Scientists are responsible for extracting insights from data to solve business problems. They use a variety of techniques, including machine learning, statistics, and data analysis. Amazon SageMaker Neo can be a powerful tool for Data Scientists who want to deploy machine learning models into production. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Data Scientists who want to enhance their skills in this area.
Cloud Architect
Cloud Architect
Cloud Architects design, build, and manage cloud computing systems. They work with clients to understand their business needs and design solutions that are scalable, reliable, and secure. Amazon SageMaker Neo is a cloud-based service that can help Cloud Architects deploy machine learning models with speed and efficiency. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Cloud Architects who want to enhance their skills in this area.
DevOps Engineer
DevOps Engineer
DevOps Engineers are responsible for bridging the gap between development and operations teams. They work to ensure that software is deployed and maintained efficiently and reliably. Amazon SageMaker Neo can be a valuable tool for DevOps Engineers who want to automate the deployment and management of machine learning models. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for DevOps Engineers who want to enhance their skills in this area.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketing teams to bring products to market that meet the needs of customers. Amazon SageMaker Neo can be a valuable tool for Product Managers who want to incorporate machine learning into their products. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Product Managers who want to enhance their skills in this area.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding the business needs of an organization and translating them into technical requirements. They work with stakeholders to gather requirements, analyze data, and develop solutions that improve business outcomes. Amazon SageMaker Neo can be a valuable tool for Business Analysts who want to understand the potential of machine learning and how it can be used to solve business problems. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Business Analysts who want to enhance their skills in this area.
Data Engineer
Data Engineer
Data Engineers are responsible for building and maintaining the infrastructure that supports data analysis and machine learning. They work with data scientists and other stakeholders to design and implement data pipelines that collect, clean, and transform data. Amazon SageMaker Neo can be a valuable tool for Data Engineers who want to deploy machine learning models into production. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Data Engineers who want to enhance their skills in this area.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers are responsible for developing new machine learning algorithms and techniques. They work in academia and industry to push the boundaries of machine learning and develop new solutions to real-world problems. Amazon SageMaker Neo can be a valuable tool for Machine Learning Researchers who want to deploy their research into production. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Machine Learning Researchers who want to enhance their skills in this area.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They work in investment banks, hedge funds, and other financial institutions. Amazon SageMaker Neo can be a valuable tool for Quantitative Analysts who want to use machine learning to enhance their investment strategies. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Quantitative Analysts who want to enhance their skills in this area.
Technical Writer
Technical Writer
Technical Writers create documentation and other materials that explain complex technical concepts to a non-technical audience. They work in various industries, including technology, healthcare, and manufacturing. Amazon SageMaker Neo can be a valuable tool for Technical Writers who want to learn about the latest advances in machine learning and how it can be used to solve real-world problems. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Technical Writers who want to enhance their skills in this area.
Educator
Educator
Educators teach students at all levels, from elementary school to university. They develop lesson plans, deliver instruction, and assess student learning. Amazon SageMaker Neo can be a valuable tool for Educators who want to teach their students about the latest advances in machine learning and how it can be used to solve real-world problems. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Educators who want to enhance their skills in this area.
Consultant
Consultant
Consultants provide advice and guidance to organizations on a variety of topics, including business strategy, technology, and finance. Amazon SageMaker Neo can be a valuable tool for Consultants who want to help their clients adopt machine learning and improve their business outcomes. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Consultants who want to enhance their skills in this area.
Sales Engineer
Sales Engineer
Sales Engineers work with customers to understand their needs and recommend solutions that meet those needs. They work in various industries, including technology, healthcare, and manufacturing. Amazon SageMaker Neo can be a valuable tool for Sales Engineers who want to learn about the latest advances in machine learning and how it can be used to solve real-world problems. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Sales Engineers who want to enhance their skills in this area.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns to promote products and services. They work in various industries, including technology, healthcare, and manufacturing. Amazon SageMaker Neo can be a valuable tool for Marketing Managers who want to learn about the latest advances in machine learning and how it can be used to reach and engage customers. The course provides an introduction to the key concepts and benefits of Amazon SageMaker Neo, making it an excellent resource for Marketing Managers who want to enhance their skills in this area.
For more career information including salaries, visit:
OpenCourser.com/course/6qc741/introduction
Reading list
We've selected 14 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
Introduction to Amazon SageMaker Neo.
Shows how to work with deep learning on AWS.
Save
Provides a comprehensive overview of machine learning concepts and techniques, including deep learning with TensorFlow. It valuable resource for both beginners and experienced practitioners.
Widely used textbook on deep learning with Python.
Covers Spark and complements the Big Data concepts in the course.
Provides a high-level overview of machine learning concepts. It valuable resource for beginners who want to gain a broad understanding of the field.
Provides a gentle introduction to AI concepts and how to apply them with Python.
Comprehensive textbook on machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and Bayesian methods.
A useful resource for those newer to data science who want to supplement the course with a practical coding-first textbook.
Save
Provides a probabilistic perspective on machine learning. It covers a wide range of topics, including Bayesian inference, graphical models, and reinforcement learning.
Provides a practical guide to machine learning for data science. It covers a wide range of topics, including data preprocessing, model selection, and evaluation.
Provides a modern perspective on deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Save
Provides a comprehensive overview of artificial intelligence. It covers a wide range of topics, including machine learning, natural language processing, and computer vision.
Provides a comprehensive overview of machine learning algorithms. It valuable resource for both beginners and experienced practitioners.
Provides a practical guide to machine learning for business. It covers a wide range of topics, including data preprocessing, model selection, and evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6qc741/introduction
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Level
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Via
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Instructor
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Language
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Good to know
Excellent course for developers who want to learn how to deploy their own ML models to multiple platforms
Taught by AWS, the leaders in cloud computing
Provides a comprehensive overview of Amazon SageMaker Neo
Covers advanced topics such as model optimization and performance tuning
Includes hands-on exercises to reinforce learning
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