MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services from edX

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course: MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services. In this course you will learn how to set up automated monitoring of your data pipeline for prediction. Data drift, model drift and feedback loops can impair model performance and model stability, and you will learn how to monitor for those phenomena. You will also learn about setting triggers and alarms, so that operators can deal with problems with model instability. You will also cover ethical issues in machine learning and the risks they pose, and learn about the "Responsible Data Science" framework.

What you'll learn

You will learn how to set up automated monitoring of your data pipeline for prediction and get hands on experience with topics like data pipelines, drift and feedback loops, model stability, triggers & alarms, model security, responsible AI and much more.

But most importantly, by the end of this course, you will know…

How to meet the differing requirements of model training versus model inference in your pipeline
How to check for model drift, data drift, and feedback loops
How to apply the principles of Continuous Integration (CI), Continuous Delivery (CDE) and Continuous Deployment (CD)

What's inside

Learning objectives

How to meet the differing requirements of model training versus model inference in your pipeline
How to check for model drift, data drift, and feedback loops

How to apply the principles of continuous integration (ci), continuous delivery (cde) and continuous deployment (cd)

How to meet the differing requirements of model training versus model inference in your pipeline
How to check for model drift, data drift, and feedback loops
How to apply the principles of continuous integration (ci), continuous delivery (cde) and continuous deployment (cd)

Syllabus

Week 1 – Drift and Feedback Loops

Module 1: Training Versus Inference Pipelines

Module 2: Drift & Feedback Loops

Week 2 – Triggers, Alarms & Model Stability

Module 3: Triggers & Alarms

Module 4: Model Stability

Week 3 – CI/CD (Continuous Integration & Continuous Deployment/Delivery)

Module 5: CI/CD

Week 4 – Model Security and Responsible AI

Module 6: Responsible AI

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by reputable instructors, this course leverages their domain expertise

Covers key topics in MLOps, including automated monitoring, data drift, model stability, and responsible AI

Meets the varying requirements of data pipeline automation and optimization

Hands-on experience with data monitoring, drift detection, and model stability

Covers essential concepts of Continuous Integration, Delivery, and Deployment

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 MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services with these activities:

Organize Course Lecture Notes and Practice Materials

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Organize and review your course materials regularly, reinforcing your understanding of key concepts and improving retention.

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Gather all lecture notes, assignments, and practice questions.
Create a filing system or digital notebook.
Review materials periodically to refresh your memory.

Review DevOps Principles and Practices

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Strengthen your understanding of DevOps principles, which are essential for implementing MLOps practices and ensuring efficient data pipeline automation.

Browse courses on DevOps

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Read articles or watch videos on DevOps methodologies.
Review case studies of successful DevOps implementations.
Attend a DevOps workshop or online training.

Review AWS Documentation on Data Pipeline Automation

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Review Amazon Web Services documentation to build a strong foundation in data pipeline automation, which will enhance your understanding of the course concepts.

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Visit the AWS Data Pipeline Automation documentation website.
Explore the sections on pipeline architecture, monitoring, and optimization.
Take notes on key concepts and best practices.

Five other activities

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Gather Best Practices and Resources for MLOps Automation

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Compile a collection of best practices, tools, and tutorials for MLOps automation, serving as a valuable reference.

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Research and identify relevant articles, white papers, and tutorials.
Organize resources in a digital or physical notebook.
Share the compilation with classmates or the course community.

Design a Data Pipeline Architecture for a Real-World Scenario

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Design a data pipeline architecture for a practical use case, applying the principles learned in the course to solidify your understanding.

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Identify a real-world business scenario that requires data processing.
Sketch out a data pipeline architecture that addresses the scenario's requirements.
Consider data sources, transformation steps, and monitoring mechanisms.

Assist Classmates with Data Pipeline Implementation

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Help fellow students with data pipeline implementation, reinforcing your own understanding while contributing to a positive learning environment.

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Identify classmates who may need assistance with data pipeline projects.
Offer your expertise and provide guidance.
Collaborate on solving technical challenges.

Monitor Data Pipelines Using AWS CloudWatch

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Practice monitoring data pipelines with AWS CloudWatch, developing proficiency in identifying and addressing performance issues.

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Create a CloudWatch dashboard for your data pipeline.
Configure metrics and alarms to monitor pipeline performance.
Simulate pipeline issues and observe the alerts generated.

Participate in an MLOps Hackathon

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Apply your MLOps knowledge and skills in a competitive setting, gaining valuable experience and feedback.

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Find an MLOps hackathon that aligns with your interests.
Form a team or work individually.
Develop an innovative data pipeline solution within the hackathon's constraints.
Present your solution to a panel of judges.

Career center

Learners who complete MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services will develop knowledge and skills that may be useful to these careers:

Data Engineer

A Data Engineer helps to design, build, and maintain data pipelines that connect various data sources and transform data into a usable format. By taking this course, you will gain the skills necessary to build automated data pipelines, which will make you a more valuable asset to any team.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

A Machine Learning Engineer builds, deploys, and maintains machine learning models. This course will provide you with the skills you need to automate the deployment and optimization of machine learning models, which will make you a more effective Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of data analysis and machine learning to solve business problems. This course will provide you with the skills you need to build and deploy machine learning models, which will make you a more effective Data Scientist.

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Data Analyst

Data Analysts use data to identify trends and patterns that can help businesses make better decisions. This course will provide you with the skills you need to use data to make better decisions.

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Business Analyst

Business Analysts use data to identify and solve business problems. This course will provide you with the skills you need to use data to make better business decisions.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course will provide you with the skills you need to build and deploy software systems, which will make you a more valuable asset to any team.

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DevOps Engineer

DevOps Engineers work to improve the collaboration between development and operations teams. This course will provide you with the skills you need to automate the deployment and optimization of software systems, which will make you a more effective DevOps Engineer.

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Data Architect

Data Architects design and implement data architectures. This course will provide you with the skills you need to build and deploy data architectures, which will make you a more valuable asset to any team.

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Cloud Architect

Cloud Architects design and implement cloud computing solutions. This course will provide you with the skills you need to build and deploy cloud computing solutions, which will make you a more valuable asset to any team.

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Systems Engineer

Systems Engineers design and implement systems solutions. This course will provide you with the skills you need to build and deploy systems solutions, which will make you a more valuable asset to any team.

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Database Administrator

Database Administrators manage and maintain databases. This course will provide you with the skills you need to build and deploy databases, which will make you a more valuable asset to any team.

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Security Engineer

Security Engineers design and implement security solutions. This course will provide you with the skills you need to build and deploy security solutions, which will make you a more valuable asset to any team.

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Network Engineer

Network Engineers design and implement network solutions. This course will provide you with the skills you need to build and deploy network solutions, which will make you a more valuable asset to any team.

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Technical Writer

Technical Writers create and maintain technical documentation. This course will provide you with the skills you need to create and maintain technical documentation, which will make you a more valuable asset to any team.

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Product Manager

Product Managers manage the development and launch of new products. This course may be useful for Product Managers who want to learn more about how to use data to make better decisions.

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