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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
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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
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Building Machine Learning Pipelines on AWS with these
activities:
Review Python Programming
Show steps
Refresh your knowledge of Python syntax and structures to prepare for this course's coverage of building machine learning pipelines.
Browse courses on
Python
Show steps
-
Set up a Python development environment
-
Review variables, data types, operators, and control flow
-
Practice writing simple Python functions
Resource Hub for AWS Machine Learning
Show steps
Organize and curate a comprehensive collection of resources, including articles, tutorials, and documentation, on AWS machine learning to enhance your knowledge and stay updated.
Browse courses on
AWS
Show steps
-
Identify and gather relevant resources from various sources.
-
Categorize and organize the resources based on topics and subtopics.
-
Share your resource hub with other learners or within a community forum.
Course Notes and Resources Review
Show steps
Refresh your knowledge by regularly reviewing your course notes, assignments, and supplemental materials to reinforce your understanding of key concepts.
Browse courses on
Review
Show steps
-
Go through your notes and identify areas where you need further clarification.
-
Review the corresponding course materials and seek additional resources to fill in any knowledge gaps.
18 other activities
Expand to see all activities and additional details
Show all 21 activities
Review SageMaker
Show steps
Revisiting SageMaker can help you achieve better learning outcomes specifically for this course, as it will provide you with a strong foundation in the core concepts and techniques used in SageMaker.
Browse courses on
SageMaker
Show steps
-
Review the SageMaker documentation
-
Complete the SageMaker tutorial
-
Build a simple machine learning model using SageMaker
SageMaker Tutorial Series
Show steps
Strengthen your foundation in AWS SageMaker by following official tutorials and documentation to become proficient in its core features and functionalities.
Browse courses on
AWS SageMaker
Show steps
-
Review the introductory materials and set up your AWS account.
-
Follow the step-by-step tutorials to create and train machine learning models.
-
Explore advanced topics such as hyperparameter tuning and model deployment.
-
Complete hands-on exercises to reinforce your understanding.
AWS CLI Exercises
Show steps
Improve your proficiency with the AWS Command Line Interface (CLI) through dedicated practice exercises, enabling you to efficiently manage and interact with AWS services.
Browse courses on
AWS
Show steps
-
Install and configure the AWS CLI on your local machine.
-
Follow tutorials and documentation to learn basic CLI commands.
-
Practice using CLI commands to create and manage AWS resources.
Host a study group
Show steps
Participating in a group setting dedicated to studying and discussing course materials can foster a deeper understanding of the subject matter, provide diverse perspectives, and enhance retention.
Browse courses on
Machine Learning Pipelines
Show steps
-
Gather a group of classmates
-
Choose a topic to discuss
-
Facilitate the discussion
Join a Study Group
Show steps
Collaborate with peers to discuss concepts, work through exercises, and reinforce your learning.
Show steps
-
Find a study group or create your own
-
Establish meeting times and set goals
-
Share notes, ask questions, and work together on assignments
Help other students in the course
Show steps
By assisting other students in the course, you can reinforce your own understanding of the material while developing valuable teaching and communication skills.
Browse courses on
Machine Learning Pipelines
Show steps
-
Join the course discussion forum
-
Answer questions posted by other students
-
Offer to help classmates with specific topics
Build a Simple Machine Learning Model
Show steps
Gain hands-on experience with machine learning concepts by building a basic model from scratch.
Show steps
-
Choose a dataset and define the problem
-
Preprocess and explore the data
-
Train a model using a simple algorithm
-
Evaluate the model's performance
AWS Machine Learning Challenge
Show steps
Challenge yourself and showcase your skills by participating in an AWS Machine Learning Challenge, which offers opportunities to solve real-world problems and compete with other participants.
Browse courses on
AWS
Show steps
-
Identify a challenge that aligns with your interests and abilities.
-
Form a team or work individually to develop a solution.
-
Build and train your machine learning model using AWS services.
Practice building machine learning pipelines
Show steps
By actively practicing building machine learning pipelines, you can significantly enhance your understanding of the concepts and techniques covered in this course.
Browse courses on
Machine Learning Pipelines
Show steps
-
Build a machine learning pipeline for a simple classification task
-
Build a machine learning pipeline for a more complex regression task
-
Deploy a machine learning pipeline to production
Follow Tutorials on SageMaker
Show steps
Enhance your understanding of SageMaker by following official or community-created tutorials.
Show steps
-
Find tutorials on AWS's documentation website
-
Select a tutorial that aligns with your learning objectives
-
Follow the instructions and complete the tutorial
End-to-End Pipeline Project
Show steps
Gain hands-on experience by building a complete end-to-end machine learning pipeline that integrates data ingestion, model training, evaluation, and deployment.
Browse courses on
CI/CD
Show steps
-
Define a real-world problem and gather the necessary data.
-
Choose and implement appropriate machine learning algorithms for your problem.
-
Build a pipeline that automates the data preparation, model training, and evaluation stages.
-
Deploy your pipeline to a cloud platform, such as AWS.
Explore advanced SageMaker features
Show steps
By seeking out and following tutorials on advanced SageMaker features, you can expand your knowledge and skills in this area, enhancing your ability to build more sophisticated machine learning pipelines.
Browse courses on
SageMaker
Show steps
-
Follow a tutorial on using SageMaker Autopilot
-
Follow a tutorial on using SageMaker Model Monitor
-
Follow a tutorial on using SageMaker Neo
Create a Glossary of Machine Learning Terms
Show steps
Solidify your understanding of key machine learning concepts by compiling a glossary of terms.
Show steps
-
Identify and list relevant machine learning terms
-
Provide clear and concise definitions
-
Organize the terms alphabetically or by category
Contribute to OpenML Project
Show steps
Engage with the broader machine learning community by contributing to open-source projects such as OpenML, where you can learn from others and make valuable contributions to the field.
Browse courses on
Machine Learning
Show steps
-
Identify an area within the OpenML project that aligns with your interests.
-
Review the project's documentation and contribution guidelines.
-
Make a code contribution, such as adding a new feature or fixing a bug.
-
Participate in community discussions and provide support to other users.
Develop a Machine Learning Pipeline Proposal
Show steps
Demonstrate your understanding of machine learning pipelines by creating a proposal for a specific project.
Show steps
-
Define the problem and business objectives
-
Describe the data sources and preprocessing steps
-
Select and justify the machine learning algorithms
-
Outline the evaluation metrics and deployment strategy
Build a machine learning pipeline for a real-world project
Show steps
Engaging in a real-world project involving machine learning pipeline construction allows you to apply your knowledge and skills in a practical setting, solidifying your understanding and enhancing your ability to tackle similar challenges in the future.
Browse courses on
Machine Learning Pipelines
Show steps
-
Identify a real-world problem that can be solved using a machine learning pipeline
-
Gather the necessary data
-
Build a machine learning pipeline to solve the problem
-
Deploy the machine learning pipeline to production
Contribute to an open-source machine learning project
Show steps
Engaging in open-source projects related to machine learning can provide valuable hands-on experience, broaden your technical skills, and contribute to the community.
Browse courses on
Machine Learning
Show steps
-
Find an open-source machine learning project to contribute to
-
Identify an area where you can make a contribution
-
Make your contribution to the project
Model Development Report
Show steps
Document your experience with the course concepts in a comprehensive report on the development and deployment of your own machine learning pipeline on AWS.
Browse courses on
AWS
Show steps
-
Choose a business problem and dataset that aligns with your interests and skills.
-
Explore different machine learning algorithms and techniques to address the problem.
-
Build and train a machine learning model using AWS SageMaker.
-
Create a machine learning pipeline on AWS to automate the model training and deployment process.
-
Deploy your model to production and monitor its performance.
Review Python Programming
Show steps
Refresh your knowledge of Python syntax and structures to prepare for this course's coverage of building machine learning pipelines.
Browse courses on
Python
Show steps
Show steps
Show steps
- Set up a Python development environment
- Review variables, data types, operators, and control flow
- Practice writing simple Python functions
Resource Hub for AWS Machine Learning
Show steps
Organize and curate a comprehensive collection of resources, including articles, tutorials, and documentation, on AWS machine learning to enhance your knowledge and stay updated.
Browse courses on
AWS
Show steps
Show steps
Show steps
- Identify and gather relevant resources from various sources.
- Categorize and organize the resources based on topics and subtopics.
- Share your resource hub with other learners or within a community forum.
Course Notes and Resources Review
Show steps
Refresh your knowledge by regularly reviewing your course notes, assignments, and supplemental materials to reinforce your understanding of key concepts.
Browse courses on
Review
Show steps
Show steps
Show steps
- Go through your notes and identify areas where you need further clarification.
- Review the corresponding course materials and seek additional resources to fill in any knowledge gaps.
18 other activities
Expand to see all activities and additional details
Show all 21 activities
Review SageMaker
Show steps
Revisiting SageMaker can help you achieve better learning outcomes specifically for this course, as it will provide you with a strong foundation in the core concepts and techniques used in SageMaker.
Browse courses on
SageMaker
Show steps
Show steps
Show steps
- Review the SageMaker documentation
- Complete the SageMaker tutorial
- Build a simple machine learning model using SageMaker
SageMaker Tutorial Series
Show steps
Strengthen your foundation in AWS SageMaker by following official tutorials and documentation to become proficient in its core features and functionalities.
Browse courses on
AWS SageMaker
Show steps
Show steps
Show steps
- Review the introductory materials and set up your AWS account.
- Follow the step-by-step tutorials to create and train machine learning models.
- Explore advanced topics such as hyperparameter tuning and model deployment.
- Complete hands-on exercises to reinforce your understanding.
AWS CLI Exercises
Show steps
Improve your proficiency with the AWS Command Line Interface (CLI) through dedicated practice exercises, enabling you to efficiently manage and interact with AWS services.
Browse courses on
AWS
Show steps
Show steps
Show steps
- Install and configure the AWS CLI on your local machine.
- Follow tutorials and documentation to learn basic CLI commands.
- Practice using CLI commands to create and manage AWS resources.
Host a study group
Show steps
Participating in a group setting dedicated to studying and discussing course materials can foster a deeper understanding of the subject matter, provide diverse perspectives, and enhance retention.
Browse courses on
Machine Learning Pipelines
Show steps
Show steps
Show steps
- Gather a group of classmates
- Choose a topic to discuss
- Facilitate the discussion
Join a Study Group
Show steps
Collaborate with peers to discuss concepts, work through exercises, and reinforce your learning.
Show steps
Show steps
Show steps
- Find a study group or create your own
- Establish meeting times and set goals
- Share notes, ask questions, and work together on assignments
Help other students in the course
Show steps
By assisting other students in the course, you can reinforce your own understanding of the material while developing valuable teaching and communication skills.
Browse courses on
Machine Learning Pipelines
Show steps
Show steps
Show steps
- Join the course discussion forum
- Answer questions posted by other students
- Offer to help classmates with specific topics
Build a Simple Machine Learning Model
Show steps
Gain hands-on experience with machine learning concepts by building a basic model from scratch.
Show steps
Show steps
Show steps
- Choose a dataset and define the problem
- Preprocess and explore the data
- Train a model using a simple algorithm
- Evaluate the model's performance
AWS Machine Learning Challenge
Show steps
Challenge yourself and showcase your skills by participating in an AWS Machine Learning Challenge, which offers opportunities to solve real-world problems and compete with other participants.
Browse courses on
AWS
Show steps
Show steps
Show steps
- Identify a challenge that aligns with your interests and abilities.
- Form a team or work individually to develop a solution.
- Build and train your machine learning model using AWS services.
Practice building machine learning pipelines
Show steps
By actively practicing building machine learning pipelines, you can significantly enhance your understanding of the concepts and techniques covered in this course.
Browse courses on
Machine Learning Pipelines
Show steps
Show steps
Show steps
- Build a machine learning pipeline for a simple classification task
- Build a machine learning pipeline for a more complex regression task
- Deploy a machine learning pipeline to production
Follow Tutorials on SageMaker
Show steps
Enhance your understanding of SageMaker by following official or community-created tutorials.
Show steps
Show steps
Show steps
- Find tutorials on AWS's documentation website
- Select a tutorial that aligns with your learning objectives
- Follow the instructions and complete the tutorial
End-to-End Pipeline Project
Show steps
Gain hands-on experience by building a complete end-to-end machine learning pipeline that integrates data ingestion, model training, evaluation, and deployment.
Browse courses on
CI/CD
Show steps
Show steps
Show steps
- Define a real-world problem and gather the necessary data.
- Choose and implement appropriate machine learning algorithms for your problem.
- Build a pipeline that automates the data preparation, model training, and evaluation stages.
- Deploy your pipeline to a cloud platform, such as AWS.
Explore advanced SageMaker features
Show steps
By seeking out and following tutorials on advanced SageMaker features, you can expand your knowledge and skills in this area, enhancing your ability to build more sophisticated machine learning pipelines.
Browse courses on
SageMaker
Show steps
Show steps
Show steps
- Follow a tutorial on using SageMaker Autopilot
- Follow a tutorial on using SageMaker Model Monitor
- Follow a tutorial on using SageMaker Neo
Create a Glossary of Machine Learning Terms
Show steps
Solidify your understanding of key machine learning concepts by compiling a glossary of terms.
Show steps
Show steps
Show steps
- Identify and list relevant machine learning terms
- Provide clear and concise definitions
- Organize the terms alphabetically or by category
Contribute to OpenML Project
Show steps
Engage with the broader machine learning community by contributing to open-source projects such as OpenML, where you can learn from others and make valuable contributions to the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify an area within the OpenML project that aligns with your interests.
- Review the project's documentation and contribution guidelines.
- Make a code contribution, such as adding a new feature or fixing a bug.
- Participate in community discussions and provide support to other users.
Develop a Machine Learning Pipeline Proposal
Show steps
Demonstrate your understanding of machine learning pipelines by creating a proposal for a specific project.
Show steps
Show steps
Show steps
- Define the problem and business objectives
- Describe the data sources and preprocessing steps
- Select and justify the machine learning algorithms
- Outline the evaluation metrics and deployment strategy
Build a machine learning pipeline for a real-world project
Show steps
Engaging in a real-world project involving machine learning pipeline construction allows you to apply your knowledge and skills in a practical setting, solidifying your understanding and enhancing your ability to tackle similar challenges in the future.
Browse courses on
Machine Learning Pipelines
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using a machine learning pipeline
- Gather the necessary data
- Build a machine learning pipeline to solve the problem
- Deploy the machine learning pipeline to production
Contribute to an open-source machine learning project
Show steps
Engaging in open-source projects related to machine learning can provide valuable hands-on experience, broaden your technical skills, and contribute to the community.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find an open-source machine learning project to contribute to
- Identify an area where you can make a contribution
- Make your contribution to the project
Model Development Report
Show steps
Document your experience with the course concepts in a comprehensive report on the development and deployment of your own machine learning pipeline on AWS.
Browse courses on
AWS
Show steps
Show steps
Show steps
- Choose a business problem and dataset that aligns with your interests and skills.
- Explore different machine learning algorithms and techniques to address the problem.
- Build and train a machine learning model using AWS SageMaker.
- Create a machine learning pipeline on AWS to automate the model training and deployment process.
- Deploy your model to production and monitor its performance.
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.
Save
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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