Machine Learning Implementation and Operations in AWS from Coursera

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

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

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

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

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

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Organizing and reviewing course materials enhances comprehension, facilitates effective studying, and provides a valuable reference for future use.

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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

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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

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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

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Engaging with peers through study groups or discussion forums allows for knowledge sharing, clarification of concepts, and diverse perspectives, enhancing understanding.

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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

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Complete online tutorials on AWS Machine Learning

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Working through guided tutorials on AWS Machine Learning enhances hands-on skills and provides practical insights into the concepts covered in the course.

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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

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Building a sample machine learning application in AWS provides hands-on experience in applying the concepts learned in the course and reinforces understanding.

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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

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Revisiting course materials and practicing problems helps solidify understanding, identify areas for improvement, and prepare for assessments.

Browse courses on Machine Learning Operations

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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

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Undertaking a personalized machine learning project allows for creative application of the concepts learned in the course, fostering a deeper understanding and practical experience.

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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

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Mentoring others allows for knowledge sharing, reinforcement of concepts, and the development of leadership and communication skills.

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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 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.

See salaries and explore the career path for Machine 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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DevOps Engineer

DevOps Engineers work with development and operations teams to ensure that software is deployed and maintained efficiently and reliably.

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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.

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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.

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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.

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