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Noah Gift
and
Alfredo Deza
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/jq0kjn/mlops
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.
Read more
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.
Read more
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
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
Save this course
Save MLOps Platforms: Amazon SageMaker and Azure ML to your list so you can find it easily later:
Save
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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 MLOps Platforms: Amazon SageMaker and Azure ML with these
activities:
Review Python fundamentals
Show steps
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
Show steps
-
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
Show steps
Connect with other learners and engage in discussions to enhance your understanding.
Show steps
-
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
Show steps
Reinforce your understanding of AWS technology by practicing using the AWS CLI.
Show steps
-
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
Expand to see all activities and additional details
Show all 12 activities
Explore Azure ML
Show steps
Develop a practical understanding of Azure ML by working through hands-on exercises.
Show steps
-
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
Show steps
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
Show steps
-
Deploy a model to an AWS EC2 instance.
-
Deploy a model to an Azure Kubernetes Service cluster.
Build MLOps pipelines on AWS and Azure
Show steps
Practice building MLOps pipelines on both AWS and Azure to enhance your understanding of the process and reinforce key concepts.
Browse courses on
MLOps
Show steps
-
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
Show steps
Seek out and follow tutorials on advanced machine learning topics to expand your knowledge and explore different techniques.
Browse courses on
Machine Learning
Show steps
-
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
Show steps
Gain practical experience in building an end-to-end MLOps pipeline using SageMaker.
Show steps
-
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
Show steps
Apply your knowledge of Azure ML to create a complete machine learning application.
Show steps
-
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
Show steps
Reflect on your learning experience and share your insights with others.
Show steps
-
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
Show steps
Contribute to the community by sharing your expertise and helping others learn.
Show steps
-
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
Show steps
Reinforce your learning and make a positive impact by helping others.
Show steps
-
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
Review Python fundamentals
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Connect with other learners and engage in discussions to enhance your understanding.
Show steps
Show steps
Show steps
- 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
Show steps
Reinforce your understanding of AWS technology by practicing using the AWS CLI.
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all 12 activities
Explore Azure ML
Show steps
Develop a practical understanding of Azure ML by working through hands-on exercises.
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- Deploy a model to an AWS EC2 instance.
- Deploy a model to an Azure Kubernetes Service cluster.
Build MLOps pipelines on AWS and Azure
Show steps
Practice building MLOps pipelines on both AWS and Azure to enhance your understanding of the process and reinforce key concepts.
Browse courses on
MLOps
Show steps
Show steps
Show steps
- 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
Show steps
Seek out and follow tutorials on advanced machine learning topics to expand your knowledge and explore different techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- 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
Show steps
Gain practical experience in building an end-to-end MLOps pipeline using SageMaker.
Show steps
Show steps
Show steps
- 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
Show steps
Apply your knowledge of Azure ML to create a complete machine learning application.
Show steps
Show steps
Show steps
- 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
Show steps
Reflect on your learning experience and share your insights with others.
Show steps
Show steps
Show steps
- 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
Show steps
Contribute to the community by sharing your expertise and helping others learn.
Show steps
Show steps
Show steps
- 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
Show steps
Reinforce your learning and make a positive impact by helping others.
Show steps
Show steps
Show steps
- 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 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.
Data Architect
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.
Cloud 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.
Software Engineer
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.
Data Scientist
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.
Statistician
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.
Data Governance Officer
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.
Data Analyst
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.
Quantitative 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.
Product Manager
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.
Database Administrator
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.
Business Analyst
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.
Operations Research 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.
Data Engineer
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.
Management Consultant
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.
For more career information including salaries, visit:
OpenCourser.com/course/jq0kjn/mlops
Reading list
We've selected nine 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
MLOps Platforms: Amazon SageMaker and Azure ML.
Provides a collection of recipes for solving common machine learning problems using Python. It covers topics such as data preprocessing, model selection, and model evaluation.
Provides a comprehensive overview of deep learning, including how to use it to build and train deep learning models. It also covers topics such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive overview of natural language processing, including how to use it to build and train natural language processing models. It also covers topics such as text classification and text generation.
Provides a comprehensive overview of computer vision, including how to use it to build and train computer vision models. It also covers topics such as image classification and object detection.
Provides a comprehensive overview of reinforcement learning, including how to use it to build and train reinforcement learning models. It also covers topics such as Q-learning and deep reinforcement learning.
Provides a comprehensive overview of machine learning, including how to use it to build and train machine learning models. It also covers topics such as data preprocessing and model evaluation.
Provides a gentle introduction to machine learning, making it accessible to beginners. It covers topics such as data preparation, model training, and model evaluation.
Provides a comprehensive overview of TensorFlow, including how to use it to build and train machine learning models. It also covers topics such as data preprocessing and model evaluation.
Provides a comprehensive overview of machine learning, including how to use it to build and train machine learning models. It also covers topics such as data preprocessing and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jq0kjn/mlops
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Effort
5 weeks of study, 3-5 hours/week
Level
Advanced
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
MLOps | Machine Learning Operations. It's comprised of four courses:
- MLOps Tools: MLflow and Hugging Face
- Python Essentials for MLOps
- DevOps, DataOps, MLOps
- MLOps Platforms: Amazon SageMaker and Azure ML (viewing)
Good to know
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
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