MLOps Platforms: Amazon SageMaker and Azure ML from Coursera

In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course is also a great resource for individuals looking to prepare for AWS or Azure machine learning certifications or who are working (or seek to work) as data scientists, software engineers, software developers, data analysts, or other roles that use machine learning.

Through a series of hands-on exercises, you will gain an intuition for basic machine learning algorithms and practical experience working with these leading Cloud platforms. By the end of the course, you will be able to deploy machine learning solutions in a production environment using AWS and Azure technology.

Week 1. Explore data engineering with AWS technology. We’ll discuss topics such as getting started with machine learning on AWS, creating data repositories, and identifying and implementing solutions for data ingestion and transformation.

Week 2. Gain basic data science skills with AWS technology. You will learn data cleaning techniques, perform feature engineering, data analysis, and data visualization for machine learning. We’ll prioritize using serverless solutions that are available on AWS to make the process more efficient.

Week 3. Learn machine learning models with AWS technology. We’ll examine how to select appropriate models for the task at hand, choose hyperparameters, train models on the platform, and evaluate models.

Week 4. Learn MLOps with AWS: the final phase of putting machine learning into production. We’ll discuss topics such as operationalizing a machine learning model, deciding between CPU and GPU, and deploying and maintaining the model.

Week 5. Learn how to work with data and machine learning in a second leading Cloud-based platform: Azure ML.

What's inside

Syllabus

Data Engineering with AWS Technology

This week you will learn how to build data engineering solutions on AWS and apply it by building a data engineering pipeline with AWS Step Functions and AWS Lambda.

Exploratory Data Analysis with AWS Technology

This week you will compose data engineering solutions using AWS technology and apply it by building data science notebooks.

Modeling with AWS Technology

This week you will compose machine learning modeling solutions using AWS technology and apply it by building a linear regression model that runs inside a command-line tool.

MLOps with AWS Technology

This week you will learn to deploy and operationalize machine learning solutions using AWS technology and apply it by fine-tuning a Hugging face model using Sagemaker Studio Lab.

Machine Learning Certifications

This week you will learn about Machine Learning certifications from the major cloud providers and how to apply them to MLOps. You will learn about services related to Machine Learning and ML Engineering tasks like AutoML and how they apply to the certifications.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for candidates preparing for AWS or Azure Machine Learning certifications

Delves into essential machine learning algorithms and cloud platforms

Taught by experienced professionals Noah Gift and Alfredo Deza

Focuses on hands-on exercises, providing practical experience in deploying machine learning solutions

Covers a comprehensive range of topics, from data engineering to MLOps

May require prior knowledge in data science and machine learning concepts

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 MLOps Platforms: Amazon SageMaker and Azure ML with these activities:

Review Python fundamentals

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Python is one of the most popular languages for data science. Refresh your skills in Python syntax, data structures, and object-oriented programming to strengthen your foundation for this course.

Browse courses on Python

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Go through Python tutorials and online courses.
Solve coding problems and practice writing Python code.
Review documentation and reference materials for Python.

Join a study group or online community

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Connect with other learners and engage in discussions to enhance your understanding.

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Search for study groups or online communities related to MLOps
Join the group and introduce yourself
Participate in discussions, ask questions, and share your insights
Collaborate on projects or study materials

Practice using AWS CLI

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Reinforce your understanding of AWS technology by practicing using the AWS CLI.

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Install the AWS CLI on your local machine
Create a new AWS account
Configure your AWS CLI with your new account credentials
Practice using basic AWS CLI commands, such as `ls`, `cd`, and `mkdir`

Nine other activities

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Explore Azure ML

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Develop a practical understanding of Azure ML by working through hands-on exercises.

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Create a free Azure account
Provision an Azure ML workspace
Create a new Azure ML project
Train a machine learning model using Azure ML
Deploy the trained model as a web service

Deploy a machine learning model using AWS and Azure

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Deploy a machine learning model to a production environment using both AWS and Azure to gain hands-on experience and demonstrate your understanding of deployment techniques.

Browse courses on Machine Learning Model Deployment

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Deploy a model to an AWS EC2 instance.
Deploy a model to an Azure Kubernetes Service cluster.

Build MLOps pipelines on AWS and Azure

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Practice building MLOps pipelines on both AWS and Azure to enhance your understanding of the process and reinforce key concepts.

Browse courses on MLOps

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Create an MLOps pipeline on AWS using SageMaker.
Create an MLOps pipeline on Azure using Azure Machine Learning.
Compare and contrast the two pipelines.

Follow tutorials on advanced machine learning topics

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Seek out and follow tutorials on advanced machine learning topics to expand your knowledge and explore different techniques.

Browse courses on Machine Learning

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Follow a tutorial on a specific machine learning algorithm.
Follow a tutorial on a specific machine learning library (e.g., TensorFlow, PyTorch).

Build an MLOps pipeline with SageMaker

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Gain practical experience in building an end-to-end MLOps pipeline using SageMaker.

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Create a new SageMaker notebook instance
Import the necessary libraries and data
Train a machine learning model
Deploy the trained model as a batch transform job
Monitor the deployed model and track its performance

Develop a machine learning application with Azure ML

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Apply your knowledge of Azure ML to create a complete machine learning application.

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Create a new Azure ML project
Design and implement the application architecture
Develop the machine learning model
Deploy the application to Azure
Monitor and maintain the deployed application

Write a blog post about your experience with MLOps

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Reflect on your learning experience and share your insights with others.

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Identify a specific topic related to MLOps
Research the topic and gather relevant information
Write the blog post, sharing your knowledge and experiences
Publish the blog post and promote it on social media

Record a video tutorial on how to use SageMaker

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Contribute to the community by sharing your expertise and helping others learn.

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Plan the content and structure of the video tutorial
Record the video tutorial, demonstrating the use of SageMaker
Edit and polish the video tutorial
Upload the video tutorial to a video sharing platform

Volunteer as a mentor for beginners learning MLOps

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Reinforce your learning and make a positive impact by helping others.

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Join a mentoring platform or reach out to individuals seeking guidance
Offer your expertise and guidance to beginners learning MLOps
Provide support, answer questions, and share resources
Collaborate on small projects or study groups

Career center

Learners who complete MLOps Platforms: Amazon SageMaker and Azure ML will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. These models can be used for a variety of tasks, such as image recognition, natural language processing, and fraud detection. This course may help Machine Learning Engineers learn about the full MLOps (Machine Learning Operations) lifecycle and how to operationalize a machine learning model on AWS.

See salaries and explore the career path for Machine Learning Engineer

Data Architect

Data Architects design and build data management systems. They may work in a variety of industries, including finance, healthcare, and retail. This course may help Data Architects with data engineering solutions and best practices.

See salaries and explore the career path for Data Architect

Cloud Architect

Cloud Architects design and build cloud computing solutions. They may work for a variety of organizations, including cloud providers, system integrators, and end-users. This course may be useful for Cloud Architects who want to learn more about machine learning on AWS or Azure.

See salaries and explore the career path for Cloud Architect

Software Engineer

Software Engineers design, develop, test, and maintain software systems. In this role, you may use your knowledge of machine learning to design innovative software solutions. This course may be useful for Software Engineers interested in learning more about machine learning.

See salaries and explore the career path for Software Engineer

Data Scientist

Data Scientists build tools and models that help us understand the world around us. They use a variety of techniques, including mathematics, statistics, and machine learning. This course may be useful for Data Scientists who wish to use AWS and Azure.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians collect, analyze, interpret, and present data. They may work in a variety of industries, including healthcare, education, and government. This course may be useful for Statisticians who wish to learn more about machine learning.

See salaries and explore the career path for Statistician

Data Governance Officer

Data Governance Officers are responsible for the governance and management of data. They may work in a variety of industries, including finance, healthcare, and retail. This course may be useful for Data Governance Officers who want to learn more about machine learning.

See salaries and explore the career path for Data Governance Officer

Data Analyst

Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They may use a variety of tools and techniques, including machine learning. This course may be useful for Data Analysts who wish to learn more about data engineering with AWS or Azure.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data. They may work in a variety of industries, including finance, insurance, and healthcare. This course may help prepare Quantitative Analysts for industry certification.

See salaries and explore the career path for Quantitative Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. They work with a variety of stakeholders, including engineers, designers, and marketers. This course may be useful for Product Managers who are working on products that use machine learning.

See salaries and explore the career path for Product Manager

Database Administrator

Database Administrators maintain and optimize databases. They may work in a variety of industries, including finance, healthcare, and retail. This course may be useful for Database Administrators who want to learn about machine learning.

See salaries and explore the career path for Database Administrator

Business Analyst

Business Analysts help businesses understand their data and make informed decisions. They may use a variety of tools and techniques, including machine learning. This course may be useful for Business Analysts who want to learn how to use AWS and Azure ML.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve problems in a variety of industries. They may work on projects such as supply chain optimization, healthcare scheduling, and financial planning. This course may help Operations Research Analysts get started in the field of machine learning.

See salaries and explore the career path for Operations Research Analyst

Data Engineer

As a Data Engineer, you design and build data management systems. These systems can be for data ingestion, storage, transformation, or data analysis. Data Engineers are vital in an organization's translational research process. This course may help you get started in this field by teaching you about data repositories, data ingestion, and data transformation.

See salaries and explore the career path for Data Engineer

Management Consultant

Management Consultants help businesses improve their performance. They may work on a variety of projects, including strategy development, organizational design, and process improvement. This course may be useful for Management Consultants who want to learn how to use machine learning to solve business problems.

See salaries and explore the career path for Management Consultant