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Now you can reduce machine learning inference costs by up to 75% by using Amazon Elastic Inference (Amazon EI).
Now you can reduce machine learning inference costs by up to 75% by using Amazon Elastic Inference (Amazon EI). This new accelerated compute service for Amazon SageMaker and Amazon EC2 enables you to add hardware acceleration to your machine learning inference in fractional sizes of a full GPU instance, so you can avoid over-provisioning GPU compute capacity. In this video, you’ll also learn about the service’s benefits and key features and see a brief demonstration.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Introduction to Amazon Elastic Inference
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Complements other training programs or courses on machine learning
Taught by industry-recognized AWS, ensuring up-to-date best practices and knowledge
Focuses on cost reduction as a key benefit
Provides a comprehensive overview of Amazon Elastic Inference (Amazon EI) and its key features
May require foundational knowledge of Amazon SageMaker and Amazon EC2 for a deeper understanding
In-depth and technical in nature, may be more suitable for experienced machine learning practitioners
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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 Elastic Inference with these
activities:
Connect with experts in Amazon Elastic Inference
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Gain valuable insights and guidance from experienced professionals in the field.
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Attend industry events or online forums focused on Amazon Elastic Inference.
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Reach out to individuals on LinkedIn or other platforms who have expertise in Amazon Elastic Inference.
Learn about Amazon Elastic Inference on AWS
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Gain a foundational understanding of Amazon Elastic Inference and its capabilities.
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-
Watch the introductory video provided in the course syllabus.
Practice using Amazon Elastic Inference
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Reinforce your understanding of Amazon Elastic Inference through hands-on practice.
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-
Follow the step-by-step instructions provided in the course materials.
-
Experiment with different configurations and parameters of Amazon Elastic Inference to observe the effects on performance.
Two other activities
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Contribute to open-source projects related to Amazon Elastic Inference
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Gain practical experience and contribute to the Amazon Elastic Inference community.
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Identify open-source projects related to Amazon Elastic Inference.
-
Review the project documentation and identify areas where you can contribute.
-
Make code contributions or provide documentation updates.
Build a project using Amazon Elastic Inference
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Demonstrate your mastery of Amazon Elastic Inference by creating a practical application.
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-
Identify a problem or opportunity that can be addressed using machine learning.
-
Design and implement a solution using Amazon Elastic Inference.
-
Evaluate the performance of your solution and make improvements as needed.
Connect with experts in Amazon Elastic Inference
Show steps
Gain valuable insights and guidance from experienced professionals in the field.
Show steps
Show steps
Show steps
- Attend industry events or online forums focused on Amazon Elastic Inference.
- Reach out to individuals on LinkedIn or other platforms who have expertise in Amazon Elastic Inference.
Learn about Amazon Elastic Inference on AWS
Show steps
Gain a foundational understanding of Amazon Elastic Inference and its capabilities.
Show steps
Show steps
Show steps
- Watch the introductory video provided in the course syllabus.
Practice using Amazon Elastic Inference
Show steps
Reinforce your understanding of Amazon Elastic Inference through hands-on practice.
Show steps
Show steps
Show steps
- Follow the step-by-step instructions provided in the course materials.
- Experiment with different configurations and parameters of Amazon Elastic Inference to observe the effects on performance.
Two other activities
Expand to see all activities and additional details
Show all five activities
Contribute to open-source projects related to Amazon Elastic Inference
Show steps
Gain practical experience and contribute to the Amazon Elastic Inference community.
Show steps
Show steps
Show steps
- Identify open-source projects related to Amazon Elastic Inference.
- Review the project documentation and identify areas where you can contribute.
- Make code contributions or provide documentation updates.
Build a project using Amazon Elastic Inference
Show steps
Demonstrate your mastery of Amazon Elastic Inference by creating a practical application.
Show steps
Show steps
Show steps
- Identify a problem or opportunity that can be addressed using machine learning.
- Design and implement a solution using Amazon Elastic Inference.
- Evaluate the performance of your solution and make improvements as needed.
Career center
Learners who complete Introduction to Amazon Elastic Inference will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and refine machine-learning models. They may also oversee the deployment of machine-learning programs. This course can equip you with the knowledge to use Elastic Inference to cut machine learning inference costs by up to 75%.
Data Analyst
Data Analyst
Data Analysts gather and analyze large quantities of data to find trends. They often use this data to solve business problems. This course could help you gain the skills to use Elastic Inference to accelerate your data analysis, leading you to better solutions.
Data Scientist
Data Scientist
Data Scientists use ML and other analytic techniques to model data and draw conclusions from it. This course helps build a foundation for Data Scientists by teaching them how to optimize computation resources for more efficient inference.
Solutions Architect
Solutions Architect
Solutions Architects design and implement technology solutions. This course would help teach you how to use Elastic Inference to save costs on machine learning workloads, a skill which would be of great use to prospective Solutions Architects.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may expand the skillset of Software Engineers by teaching them how to effectively utilize Elastic Inference for better results.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use math, statistics, and computer programming for financial modeling. This course could help equip you with the tools to manage costs by optimizing your use of computational resources which would be of use to Quantitative Analysts.
Business Analyst
Business Analyst
Business Analysts identify and solve business problems. This course could be useful to Business Analysts by providing insights into how to reduce costs on cloud computing, a valuable skill for optimizing business outcomes.
Product Manager
Product Manager
Product Managers oversee the development and marketing of products. This course might be helpful for Product Managers looking to build efficient, cost-effective ML-related products, as it provides a foundation on how to optimize machine learning workloads.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. This course would provide them with a chance to learn Elastic Inference, which has applications in improving data processing efficiency.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course may be helpful for them, as it could provide them with skills in optimizing machine learning processes, which heavily rely on statistics.
Market Researcher
Market Researcher
Market Researchers study market conditions and trends. This course may be useful for Market Researchers by teaching them how to optimize computational resources, a skill which could improve overall efficiency.
Financial Analyst
Financial Analyst
Financial Analysts use data to advise clients on financial decisions. This course may be useful to Financial Analysts as it teaches them how to save money on cloud computing, a skill which could lead to value for their clients.
Teacher
Teacher
Teachers instruct students in a variety of subjects. This course could be helpful for Teachers as it would give them a foundation to teach students about optimizing computational resources for machine learning, a topic that may arise in their classes
Consultant
Consultant
Consultants advise clients on a wide variety of business matters. This course could be useful to them, as it could teach them how to identify and solve problems related to optimizing cloud computing costs for their clients.
Writer
Writer
Writers create content for websites, books, and other media. This course may be helpful for Writers as it could help them gain a better understanding of how to use machine learning to optimize their work, a growing field.
For more career information including salaries, visit:
OpenCourser.com/course/2df9ol/introduction
Reading list
We've selected seven 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 Elastic Inference.
Covers the basics of computer vision, with a focus on using Keras. It includes topics such as image classification, object detection, and image segmentation.
Provides a comprehensive overview of machine learning for anomaly detection, with a focus on using Python and scikit-learn. It covers topics such as anomaly detection techniques, model evaluation, and real-world applications.
Provides a comprehensive overview of generative adversarial networks (GANs), with a focus on their theory and applications. It covers topics such as GAN architectures, training techniques, and evaluation metrics.
Provides a comprehensive overview of deep reinforcement learning, with a focus on its theory and applications. It covers topics such as reinforcement learning algorithms, deep neural networks, and real-world applications.
Provides a hands-on introduction to machine learning, with a focus on using Python and popular machine learning libraries such as scikit-learn, Keras, and TensorFlow. It covers topics such as data preparation, model training, and model evaluation.
Provides a gentle introduction to machine learning, with a focus on using Python and popular machine learning libraries such as scikit-learn. It covers topics such as data preparation, model training, and model evaluation.
Provides a comprehensive overview of machine learning, with a focus on using R and popular machine learning libraries such as caret and mlr. It covers topics such as data preparation, model training, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2df9ol/introduction
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Time
11 hours
Level
Introductory
Via
Pluralsight
Instructor
AWS
Language
English
Good to know
Complements other training programs or courses on machine learning
Taught by industry-recognized AWS, ensuring up-to-date best practices and knowledge
Focuses on cost reduction as a key benefit
Provides a comprehensive overview of Amazon Elastic Inference (Amazon EI) and its key features
May require foundational knowledge of Amazon SageMaker and Amazon EC2 for a deeper understanding
In-depth and technical in nature, may be more suitable for experienced machine learning practitioners
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