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Machine Learning Implementation Operations in AWS is the fifth Course in the AWS Certified Machine Learning Specialty specialization. The course has a major focus on designing and implementing machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 1:00-1:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
Read more
Machine Learning Implementation Operations in AWS is the fifth Course in the AWS Certified Machine Learning Specialty specialization. The course has a major focus on designing and implementing machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 1:00-1:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
Read more
Machine Learning Implementation Operations in AWS is the fifth Course in the AWS Certified Machine Learning Specialty specialization. The course has a major focus on designing and implementing machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 1:00-1:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
Module 1: Machine Learning Implementation Operations in AWS-Part 1
Module 2: Machine Learning Implementation Operations in AWS-Part 2
Minimum two year of hands-on experience in architecting, building or running ML/deep learning workloads on the AWS Cloud. By the end of this course, Learners will be able to :
-Design machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance
-Implement appropriate machine learning services and features for a given problem
-Develop machine learning solutions with lab
Register for this course and see more details by visiting:
OpenCourser.com/course/9szyba/machine
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What's inside
Syllabus
Machine Learning Implementation and Operations in AWS-Part 1
Welcome to Week 1 of Machine Learning Implementation Operations in AWS Course. This week, we'll get Introduction to the concept of building machine learning solutions. We'll Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. By the end of this week, we'll Recommend and implement the appropriate machine learning services and features for a given problem
Read more
Syllabus
Machine Learning Implementation and Operations in AWS-Part 1
Welcome to Week 1 of Machine Learning Implementation Operations in AWS Course. This week, we'll get Introduction to the concept of building machine learning solutions. We'll Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. By the end of this week, we'll Recommend and implement the appropriate machine learning services and features for a given problem
Read more
Machine Learning Implementation and Operations in AWS-Part 2
Welcome to Week 2 of Machine Learning Implementation Operations in AWS Course. this week we'll Apply basic AWS security practices to machine learning solutions.We'll also Deploy and operationalize machine learning solutions with lab. By the end of last week, we'll be in position to Summarise overall learning of Machine Learning Implementation Operations in AWS Course
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for students with a minimum of two years of hands-on experience architecting, building, or running machine learning/deep learning workloads on the AWS Cloud
Provides hands-on learning experiences through lab exercises
Taught by experienced instructors, Whizlabs Instructors, who are recognized for their work in the field of machine learning
Covers essential topics in machine learning implementation and operations on AWS, including design, implementation, deployment, and operationalization
Part of a larger specialization, AWS Certified Machine Learning Specialty, providing a comprehensive learning path in machine learning on AWS
Requires students to have prior knowledge and experience in machine learning and AWS
Save this course
Save Machine Learning Implementation and Operations in AWS 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 Implementation and Operations in AWS with these
activities:
Organize and review course materials
Show steps
Organizing and reviewing course materials enhances comprehension, facilitates effective studying, and provides a valuable reference for future use.
Show steps
-
Gather all course materials, including notes, assignments, quizzes, and exams.
-
Organize the materials logically, creating a system that makes it easy to find and access information.
Review and identify key concepts
Show steps
Reviewing key concepts in machine learning operations and cloud computing will provide a solid foundation for understanding the concepts covered in this course.
Browse courses on
Machine Learning Operations
Show steps
-
Review basic machine learning concepts such as supervised learning, unsupervised learning, and deep learning.
-
Explore the AWS cloud platform and familiarize yourself with key services such as EC2, S3, and Lambda.
Join a study group or discussion forum
Show steps
Engaging with peers through study groups or discussion forums allows for knowledge sharing, clarification of concepts, and diverse perspectives, enhancing understanding.
Show steps
-
Identify and join an online or in-person study group focused on AWS Machine Learning.
-
Participate actively in discussions, ask questions, and share your insights.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete online tutorials on AWS Machine Learning
Show steps
Working through guided tutorials on AWS Machine Learning enhances hands-on skills and provides practical insights into the concepts covered in the course.
Show steps
-
Identify reputable online platforms or resources that offer tutorials on AWS Machine Learning.
-
Select tutorials that align with your learning objectives and skill level.
-
Follow the tutorials step-by-step, implementing the concepts and experimenting with different configurations.
Develop a sample machine learning application in AWS
Show steps
Building a sample machine learning application in AWS provides hands-on experience in applying the concepts learned in the course and reinforces understanding.
Show steps
-
Define a problem statement and identify the appropriate AWS services to use.
-
Design and implement the machine learning pipeline, including data preprocessing, model training, and evaluation.
-
Deploy and test the application, monitoring its performance and making adjustments as needed.
Revisit course materials and practice problems
Show steps
Revisiting course materials and practicing problems helps solidify understanding, identify areas for improvement, and prepare for assessments.
Browse courses on
Machine Learning Operations
Show steps
-
Review lecture notes, textbooks, and other course resources.
-
Attempt practice problems and exercises to test your understanding and identify areas where you need more practice.
Develop a personalized machine learning project
Show steps
Undertaking a personalized machine learning project allows for creative application of the concepts learned in the course, fostering a deeper understanding and practical experience.
Show steps
-
Identify a problem or opportunity that aligns with your interests or career goals.
-
Research and gather the necessary data and resources.
-
Design and implement the machine learning solution, experimenting with different approaches and techniques.
Mentor junior or aspiring machine learning enthusiasts
Show steps
Mentoring others allows for knowledge sharing, reinforcement of concepts, and the development of leadership and communication skills.
Show steps
-
Identify opportunities to mentor junior learners or those interested in machine learning.
-
Share your knowledge and experience, guiding them through concepts, projects, and career development.
Organize and review course materials
Show steps
Organizing and reviewing course materials enhances comprehension, facilitates effective studying, and provides a valuable reference for future use.
Show steps
Show steps
Show steps
- Gather all course materials, including notes, assignments, quizzes, and exams.
- Organize the materials logically, creating a system that makes it easy to find and access information.
Review and identify key concepts
Show steps
Reviewing key concepts in machine learning operations and cloud computing will provide a solid foundation for understanding the concepts covered in this course.
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Review basic machine learning concepts such as supervised learning, unsupervised learning, and deep learning.
- Explore the AWS cloud platform and familiarize yourself with key services such as EC2, S3, and Lambda.
Join a study group or discussion forum
Show steps
Engaging with peers through study groups or discussion forums allows for knowledge sharing, clarification of concepts, and diverse perspectives, enhancing understanding.
Show steps
Show steps
Show steps
- Identify and join an online or in-person study group focused on AWS Machine Learning.
- Participate actively in discussions, ask questions, and share your insights.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Complete online tutorials on AWS Machine Learning
Show steps
Working through guided tutorials on AWS Machine Learning enhances hands-on skills and provides practical insights into the concepts covered in the course.
Show steps
Show steps
Show steps
- Identify reputable online platforms or resources that offer tutorials on AWS Machine Learning.
- Select tutorials that align with your learning objectives and skill level.
- Follow the tutorials step-by-step, implementing the concepts and experimenting with different configurations.
Develop a sample machine learning application in AWS
Show steps
Building a sample machine learning application in AWS provides hands-on experience in applying the concepts learned in the course and reinforces understanding.
Show steps
Show steps
Show steps
- Define a problem statement and identify the appropriate AWS services to use.
- Design and implement the machine learning pipeline, including data preprocessing, model training, and evaluation.
- Deploy and test the application, monitoring its performance and making adjustments as needed.
Revisit course materials and practice problems
Show steps
Revisiting course materials and practicing problems helps solidify understanding, identify areas for improvement, and prepare for assessments.
Browse courses on
Machine Learning Operations
Show steps
Show steps
Show steps
- Review lecture notes, textbooks, and other course resources.
- Attempt practice problems and exercises to test your understanding and identify areas where you need more practice.
Develop a personalized machine learning project
Show steps
Undertaking a personalized machine learning project allows for creative application of the concepts learned in the course, fostering a deeper understanding and practical experience.
Show steps
Show steps
Show steps
- Identify a problem or opportunity that aligns with your interests or career goals.
- Research and gather the necessary data and resources.
- Design and implement the machine learning solution, experimenting with different approaches and techniques.
Mentor junior or aspiring machine learning enthusiasts
Show steps
Mentoring others allows for knowledge sharing, reinforcement of concepts, and the development of leadership and communication skills.
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners or those interested in machine learning.
- Share your knowledge and experience, guiding them through concepts, projects, and career development.
Career center
Learners who complete Machine Learning Implementation and Operations in AWS will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, implement, and maintain machine learning models. They use their knowledge of data science and machine learning to create algorithms that can learn from data and make predictions.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design and build deep learning models. They work with other engineers and scientists to create systems that can learn from data and make predictions using deep learning techniques.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and build artificial intelligence systems. They work with other engineers and scientists to create systems that can learn from data and make predictions.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work with other researchers and engineers to create new ways to use machine learning to solve problems.
Data Scientist
Data Scientist
Data Scientists create mathematical and statistical models that can be used to predict future trends or outcomes. They use large and complex datasets to identify patterns and correlations, which can then be used to make informed decisions.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines that collect, process, and store data. They work with data scientists and data analysts to ensure that data is clean, accurate, and accessible.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use their knowledge of data analysis techniques to create reports and visualizations that can be used to make informed decisions.
Data Architect
Data Architect
Data Architects design and manage data architectures. They work with data scientists and data analysts to create data models that meet the needs of their organization.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify and solve business problems. They use their knowledge of business processes and data analysis to create solutions that improve efficiency and profitability.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud-based infrastructure. They work with cloud providers to create scalable, reliable, and secure systems that meet the needs of their organization.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. They work with engineers, designers, and marketers to create products that meet the needs of their customers.
DevOps Engineer
DevOps Engineer
DevOps Engineers work with development and operations teams to ensure that software is deployed and maintained efficiently and reliably.
Security Engineer
Security Engineer
Security Engineers design and implement security measures to protect data and systems from unauthorized access. They work with other IT professionals to ensure that security measures are effective and compliant with regulations.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders to define project scope, timelines, and budgets, and they ensure that projects are completed on time and within budget.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages and software development principles to create software that meets the needs of their users.
For more career information including salaries, visit:
OpenCourser.com/course/9szyba/machine
Reading list
We've selected 15 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 Implementation and Operations in AWS.
Comprehensive guide to machine learning on AWS. It covers a wide range of topics, including data preparation, model training, deployment, and monitoring.
Provides a comprehensive introduction to pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to speech and language processing. It covers a wide range of topics, including speech recognition, natural language understanding, and speech synthesis.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees.
Save
Provides a comprehensive introduction to machine learning with R. It covers a wide range of topics, including data manipulation, model training, and evaluation.
Save
Provides a comprehensive introduction to computer vision. It covers a wide range of topics, including image processing, object recognition, and scene understanding.
Provides a collection of recipes for machine learning tasks in Python. It covers a wide range of topics, including data preprocessing, model training, and evaluation.
Provides a practical introduction to machine learning. It covers a wide range of topics, including data preprocessing, model training, and evaluation.
Comprehensive guide to deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive introduction to natural language processing with Python. It covers a wide range of topics, including text preprocessing, text classification, and sentiment analysis.
Provides a comprehensive introduction to machine learning with R. It covers a wide range of topics, including data preparation, model training, and evaluation.
Provides a comprehensive overview of machine learning and how to use AWS services to build and deploy machine learning solutions. It covers topics such as data engineering, model training, and deployment.
Gentle introduction to machine learning with Python. It covers a wide range of topics, including data preparation, model training, and evaluation.
Is designed for beginners who want to get started with machine learning on AWS. It provides a gentle introduction to machine learning concepts and shows you how to use AWS services to build and deploy machine learning models.
Very basic introduction to machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9szyba/machine
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Effort
2 Weeks of Study, 2-2:30 hours/Week
Level
Introductory
Via
Coursera
Institution
Whizlabs
Instructor
Whizlabs Instructor
Language
English
This course belongs to a
collection
called
Exam Prep MLS-C01: AWS Certified Specialty Machine Learning. It's comprised of five courses:
- ML Algorithms
- Machine Learning Implementation and Operations in AWS (viewing)
- Exploratory Data Analysis in AWS
- Data Engineering in AWS
- Modeling in AWS
Good to know
Builds a strong foundation for students with a minimum of two years of hands-on experience architecting, building, or running machine learning/deep learning workloads on the AWS Cloud
Provides hands-on learning experiences through lab exercises
Taught by experienced instructors, Whizlabs Instructors, who are recognized for their work in the field of machine learning
Covers essential topics in machine learning implementation and operations on AWS, including design, implementation, deployment, and operationalization
Part of a larger specialization, AWS Certified Machine Learning Specialty, providing a comprehensive learning path in machine learning on AWS
Requires students to have prior knowledge and experience in machine learning and AWS
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