MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning 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 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning. 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

Delves into the ethical implications and risks associated with machine learning, offering valuable insights for responsible data science practices

Suitable for individuals seeking to develop their knowledge and skills in MLOps, with a specific focus on Azure Machine Learning

Geared towards data scientists and engineers who possess some foundational understanding of machine learning and its applications

Provides guidance on setting up automated monitoring systems to detect data and model drift, ensuring data integrity and model stability

Emphasizes the principles of Continuous Integration, Continuous Delivery, and Continuous Deployment, essential for streamlining MLOps processes

Involves hands-on experience with various topics, including data pipelines, drift, feedback loops, model stability, triggers, alarms, and responsible AI

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 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning with these activities:

Review Azure Machine Learning concepts

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Brush up on the fundamentals of Azure Machine Learning to ensure a solid foundation for the course.

Browse courses on Azure Machine Learning

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Revisit documentation and tutorials
Complete beginner-level online courses
Review Azure Machine Learning case studies

Explore Azure Machine Learning documentation

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Familiarize yourself with the Azure Machine Learning platform through official documentation and tutorials.

Browse courses on Azure Machine Learning

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Read through Azure Machine Learning documentation
Follow step-by-step tutorials

Attend an Azure Machine Learning workshop

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Engage with experts and peers at an Azure Machine Learning workshop to enhance your understanding.

Browse courses on Azure Machine Learning

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Research and identify relevant workshops
Register and attend the workshop
Actively participate in discussions and exercises

Two other activities

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Practice MLOps Concepts Using Azure Resources

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Reinforce your understanding of MLOps concepts by applying them to practical scenarios in Azure.

Browse courses on Machine Learning Operations

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Create an Azure Machine Learning workspace
Develop and train a machine learning model
Deploy the model as a web service
Monitor and optimize the model's performance

Experiment with Azure Machine Learning SDK

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Gain hands-on experience by building and deploying models using the Azure Machine Learning SDK.

Browse courses on Azure Machine Learning

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Set up Azure Machine Learning environment
Create and train models with Azure Machine Learning SDK
Deploy models as web services
Monitor and evaluate models

Career center

Learners who complete MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, you will perform a variety of tasks related to data analysis, machine learning, and modeling. You will need to be able to understand and interpret data, build and train machine learning models, and communicate your findings to stakeholders. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning can help you develop the skills you need to be successful in this role. This course will teach you how to automate your data pipeline functions and continuously optimize its performance. You will also learn about ethical issues in machine learning and the risks they pose.

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Machine Learning Engineer

As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying machine learning models. You will need to have a strong understanding of machine learning algorithms, as well as experience with data engineering and software development. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning can help you develop the skills you need to be successful in this role. This course will teach you how to automate your data pipeline functions and continuously optimize its performance. You will also learn about ethical issues in machine learning and the risks they pose.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist Manager

As a Data Scientist Manager, you will be responsible for leading a team of data scientists. You will need to have a strong understanding of data science principles, as well as experience with managing teams. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning can help you develop the skills you need to be successful in this role. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

See salaries and explore the career path for Data Scientist Manager

Chief Data Scientist

As a Chief Data Scientist, you will be responsible for leading the data science function for an organization. You will need to have a strong understanding of data science principles, as well as experience with leading teams and managing budgets. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning can help you develop the skills you need to be successful in this role. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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Machine Learning Team Lead

As a Machine Learning Team Lead, you will be responsible for leading a team of machine learning engineers. You will need to have a strong understanding of machine learning principles, as well as experience with leading teams. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning can help you develop the skills you need to be successful in this role. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

See salaries and explore the career path for Machine Learning Team Lead

Data Engineer

As a Data Engineer, you will be responsible for building and maintaining data pipelines. You will need to have a strong understanding of data engineering principles, as well as experience with data engineering tools and technologies. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning can help you develop the skills you need to be successful in this role. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

See salaries and explore the career path for Data Engineer

Data Analyst

As a Data Analyst, you will be responsible for collecting, cleaning, and analyzing data. You will need to be able to identify trends and patterns in data, and communicate your findings to stakeholders. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning can help you develop the skills you need to be successful in this role. This course will teach you how to automate your data pipeline functions and continuously optimize its performance. You will also learn about ethical issues in machine learning and the risks they pose.

See salaries and explore the career path for Data Analyst

Software Engineer

As a Software Engineer, you will be responsible for designing, developing, and testing software applications. You will need to have a strong understanding of computer science fundamentals, as well as experience with software development tools and technologies. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning may be useful for you if you are interested in developing software applications for machine learning. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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Consultant

As a Consultant, you will be responsible for providing advice and guidance to clients. You will need to have a strong understanding of the client's business, as well as experience with consulting. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning may be useful for you if you are interested in using machine learning to provide advice and guidance to clients. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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Cloud Architect

As a Cloud Architect, you will be responsible for designing and implementing cloud computing solutions. You will need to have a strong understanding of cloud computing principles, as well as experience with cloud computing technologies. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning may be useful for you if you are interested in designing and implementing cloud computing solutions for machine learning. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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Quantitative Analyst

As a Quantitative Analyst, you will be responsible for using mathematical and statistical models to analyze financial data. You will need to have a strong understanding of mathematics and statistics, as well as experience with financial data analysis. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning may be useful for you if you are interested in using machine learning to analyze financial data. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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Risk Manager

As a Risk Manager, you will be responsible for identifying and managing risks. You will need to have a strong understanding of risk management principles, as well as experience with risk management tools and techniques. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning may be useful for you if you are interested in using machine learning to identify and manage risks. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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Business Analyst

As a Business Analyst, you will be responsible for analyzing business processes and identifying opportunities for improvement. You will need to have a strong understanding of business principles, as well as experience with data analysis and modeling techniques. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning may be useful for you if you are interested in using machine learning to improve business processes. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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Statistician

As a Statistician, you will be responsible for collecting, analyzing, and interpreting data. You will need to have a strong understanding of statistics, as well as experience with data analysis and modeling techniques. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning may be useful for you if you are interested in using machine learning to collect, analyze, and interpret data. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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Product Manager

As a Product Manager, you will be responsible for managing the development and launch of new products. You will need to have a strong understanding of product management principles, as well as experience with product development and marketing. MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning may be useful for you if you are interested in using machine learning to develop new products. This course will teach you how to automate your data pipeline functions and continuously optimize its performance.

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