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Building Machine Learning Pipelines on AWS
Ivan Mushketyk
Ivan Mushketyk
In this course, *Building Machine Learning Pipelines on AWS*, you’ll learn to automate machine learning projects on AWS. First, you’ll explore how to train machine learning models using SageMaker. Next, you’ll discover how to build more and more complex machine learning pipelines on AWS. Finally, you’ll learn how to extend these pipelines and leverage other AWS services. When you’re finished with this course, you’ll have the skills and knowledge of building machine learning pipelines with AWS needed to create automated machine learning workflows.
Register for this course and see more details by visiting:
OpenCourser.com/course/bwsflb/building
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers techniques that are standard in industry
Teaches project automation techniques on AWS, which helps learners innovate in their projects
Explores increasingly complex machine learning pipelines on AWS, which helps may accelerate model creation
Taught by Ivan Mushketyk, who is recognized for their work in machine learning
Develops proficiency building machine learning pipelines with AWS, which are core skills for data scientists
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Activities
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Building Machine Learning Pipelines on AWS.
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Career center
Learners who complete Building Machine Learning Pipelines on AWS will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists utilize their knowledge of machine learning and artificial intelligence to make meaningful insights from large and unstructured data. Building Machine Learning Pipelines on AWS offers an excellent opportunity to learn the tools and techniques used by Data Scientists, helping you not only build a foundation but also upskill and advance into this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers have a strong understanding of machine learning algorithms and techniques. As such, the Building Machine Learning Pipelines on AWS course can be a useful addition to your learning journey by providing a comprehensive understanding of how to automate machine learning projects on AWS. This course may also help you stay up-to-date with the latest trends and best practices.
Software Engineer
Software Engineer
Software Engineers apply their skills to design and build software systems. Taking the Building Machine Learning Pipelines on AWS course will help you gain an understanding of the AWS ecosystem and how to leverage it to develop robust machine learning solutions. This course may be particularly beneficial if you are interested in specializing in software engineering for machine learning and AI systems.
Cloud Architect
Cloud Architect
Cloud Architects design, build, and maintain cloud-based solutions. The Building Machine Learning Pipelines on AWS course will provide you with a solid understanding of AWS services and how they can be used to create scalable and efficient machine learning pipelines. This will prove invaluable whether you are working on cloud-based machine learning projects or developing cloud-based solutions for other domains.
Data Engineer
Data Engineer
Data Engineers have the skills to build and maintain data pipelines. This Building Machine Learning Pipelines on AWS course can provide a solid foundation for understanding how to design and build data pipelines in the cloud. The course covers the necessary tools and techniques to efficiently manage and process large volumes of data.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts are responsible for analyzing data and providing insights to support decision-making. By taking the Building Machine Learning Pipelines on AWS course, you will gain valuable knowledge in machine learning algorithms and cloud computing, which are increasingly used in business intelligence. This course will help you stay ahead of the curve and leverage these technologies to extract actionable insights from data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to solve complex problems in finance and other industries. The Building Machine Learning Pipelines on AWS course will provide you with essential skills in machine learning, cloud computing, and data analysis that are highly sought after in this field. By completing this course, you will be well-equipped to contribute to the development and application of machine learning models in the financial industry.
Research Scientist
Research Scientist
Research Scientists conduct research on various scientific topics. If your research involves machine learning and cloud computing, taking the Building Machine Learning Pipelines on AWS course may be beneficial. This course will provide you with the necessary tools and skills to effectively design, implement, and evaluate machine learning pipelines in the cloud.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. If you are interested in working on machine learning-powered products, taking the Building Machine Learning Pipelines on AWS course will be beneficial. This course will help you understand how to incorporate machine learning into product development and how to build and manage machine learning pipelines effectively.
Consultant
Consultant
Consultants provide expert advice and guidance to organizations on a variety of topics, such as business strategy, technology implementation, and risk management. If you aspire to be a consultant in the field of machine learning and cloud computing, taking the Building Machine Learning Pipelines on AWS course will provide you with a solid foundation in the subject matter. This course covers the essential concepts and practices of building and managing machine learning pipelines on AWS, which are in high demand among organizations today.
Data Analyst
Data Analyst
Data Analysts analyze and interpret data to extract meaningful insights. The Building Machine Learning Pipelines on AWS course provides a solid foundation in machine learning and cloud computing, which are increasingly important skills for Data Analysts. By completing this course, you will be able to effectively analyze and interpret data, build machine learning models, and deploy them on the cloud.
DevOps Engineer
DevOps Engineer
DevOps Engineers work to bridge the gap between software development and operations teams. If you want to specialize in DevOps for machine learning, taking the Building Machine Learning Pipelines on AWS course will be advantageous. This course will teach you the tools and techniques to build, deploy, and maintain machine learning pipelines on AWS, enabling you to contribute effectively to the DevOps process within a machine learning context.
IT Architect
IT Architect
IT Architects design and implement IT systems. The Building Machine Learning Pipelines on AWS course can provide you with a strong understanding of machine learning and cloud computing, which are becoming increasingly important in IT architecture. By completing this course, you will be able to design and implement IT systems that leverage machine learning to solve complex problems.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. If you are interested in pursuing a career in machine learning research, taking the Building Machine Learning Pipelines on AWS course will provide you with a solid foundation in the subject matter. This course covers the fundamental principles of machine learning and provides hands-on experience with building and evaluating machine learning models on AWS.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. The Building Machine Learning Pipelines on AWS course will provide you with the skills and knowledge needed to build and deploy machine learning solutions on AWS. This course will teach you how to use AWS services to train, deploy, and manage machine learning models, making you a valuable asset to any software development team working on machine learning projects.
For more career information including salaries, visit:
OpenCourser.com/course/bwsflb/building
Reading list
We've selected ten 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
Building Machine Learning Pipelines on AWS.
Provides a practical guide to using AWS for machine learning projects. It covers a wide range of topics, including data preparation, model training, and deployment. It is particularly useful for learners who are new to AWS or who want to learn more about how to use it for machine learning.
Provides a comprehensive overview of deep learning, covering the basic concepts as well as more advanced topics. It is particularly useful for learners who want to learn about the theory and practice of deep learning.
Provides a comprehensive overview of machine learning algorithms, covering a wide range of topics from supervised to unsupervised learning. It is particularly useful for learners who want to learn about the different algorithms used in machine learning and how to apply them to real-world problems.
Provides a comprehensive overview of data science, covering the entire process from data collection to model deployment. It is particularly useful for learners who are new to data science or who want to learn about the best practices and tools used in the field.
Provides a comprehensive overview of statistical learning, covering a wide range of topics from supervised to unsupervised learning. It is particularly useful for learners who want to learn about the theory and practice of statistical learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics from supervised to unsupervised learning. It is particularly useful for learners who want to learn about the theory and practice of pattern recognition and machine learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering a wide range of topics from supervised to unsupervised learning. It is particularly useful for learners who want to learn about the theory and practice of machine learning from a probabilistic perspective.
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Provides a comprehensive overview of deep learning, covering a wide range of topics from the basics to the latest advances. It is particularly useful for learners who want to learn about the theory and practice of deep learning.
Provides a comprehensive overview of machine learning using R, covering a wide range of topics from supervised to unsupervised learning. It is particularly useful for learners who want to learn about the theory and practice of machine learning using R.
Provides a comprehensive overview of machine learning for hackers, covering a wide range of topics from the basics to the latest advances. It is particularly useful for learners who want to learn about the theory and practice of machine learning from a practical perspective.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bwsflb/building
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Time
120 hours
Level
Advanced
Via
Pluralsight
Instructor
Ivan Mushketyk
Language
English
Good to know
Covers techniques that are standard in industry
Teaches project automation techniques on AWS, which helps learners innovate in their projects
Explores increasingly complex machine learning pipelines on AWS, which helps may accelerate model creation
Taught by Ivan Mushketyk, who is recognized for their work in machine learning
Develops proficiency building machine learning pipelines with AWS, which are core skills for data scientists
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