Build and Operate Machine Learning Solutions with Azure from Coursera

Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions.

This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam.

The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam.

This Specialization is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

What's inside

Syllabus

Use the Azure Machine Learning SDK to train a model

Azure Machine Learning provides a cloud-based platform for training, deploying, and managing machine learning models. In this module, you will learn how to provision an Azure Machine Learning workspace. You will use tools and interfaces to work with Azure Machine Learning and run code-based experiments in an Azure Machine Learning workspace. finally, you will learn how to use Azure Machine Learning to train a model and register it in a workspace.

Work with Data and Compute in Azure Machine Learning

Data is the foundation of machine learning. In this module, you will learn how to work with datastores and datasets in Azure Machine Learning, enabling you to build scalable, cloud-based model training solutions. You'll also learn how to use cloud compute in Azure Machine Learning to run training experiments at scale.

Orchestrate pipelines and deploy real-time machine learning services with Azure Machine Learning

Orchestrating machine learning training with pipelines is a key element of DevOps for machine learning. In this module, you'll learn how to create, publish, and run pipelines to train models in Azure Machine Learning. You'll also learn how to register and deploy ML models with the Azure Machine Learning service.

Deploy batch inference pipelines and tune hyperparameters with Azure Machine Learning

Machine learning models are often used to generate predictions from large numbers of observations in a batch process. You will accomplish this using Azure Machine Learning to publish a batch inference pipeline. You will also leverage cloud-scale experiments to choose optimal hyperparameter values for model training.

Select models and protect sensitive data

In this module, you will learn how to use automated machine learning in Azure Machine Learning to find the best model for your data. You will learn how differential privacy is a leading edge approach that enables useful analysis while protecting individually identifiable data values. You will also learn about the factors that influence the predictions models make.

Monitor machine learning deployments

Machine learning models can often encapsulate unintentional bias that results in unfairness. In this module, you will learn how to use Fairlearn and Azure Machine Learning to detect and mitigate unfairness in your models. You will learn how to use telemetry to understand how a machine learning model is being used once it has been deployed into production. Finally, you will learn how to monitor data drift to ensure your model continues to predict accurately.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow

Prepares learners to take the DP-100: Designing and Implementing a Data Science Solution on Azure certification exam

Teaches learners to create end-to-end solutions in Microsoft Azure

Develops skills in managing data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure

Covers topics such as using Azure Machine Learning SDK to train a model, working with data and compute in Azure Machine Learning, orchestrating pipelines and deploying real-time machine learning services with Azure Machine Learning, deploying batch inference pipelines and tuning hyperparameters with Azure Machine Learning, selecting models and protecting sensitive data, and monitoring machine learning deployments

Part of a five-course program that prepares learners to take the DP-100: Designing and Implementing a Data Science Solution on Azure certification exam

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 Build and Operate Machine Learning Solutions with Azure with these activities:

Azure ML Study Notes

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Enhance understanding and retention by organizing and reviewing key course materials.

Browse courses on Machine Learning

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Gather lecture notes, slides, and other course materials
Summarize and consolidate information into a coherent study guide
Use active recall techniques to test understanding and identify areas for improvement

Review Advanced Machine Learning Techniques

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Review key concepts related to advanced machine learning techniques to enhance comprehension and retention.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the book's introductory chapters on advanced ML algorithms and concepts.
Summarize the main ideas and techniques discussed in each chapter.
Identify real-world examples of where these techniques have been successfully applied.

Azure ML Community Workshop

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Engage with industry experts and peers by attending Azure ML community workshops to expand knowledge and network.

Browse courses on Machine Learning

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Identify and register for relevant Azure ML workshops
Actively participate in discussions and ask questions
Network with fellow attendees and potential mentors
Follow up with workshop organizers or speakers for further learning opportunities

Five other activities

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Azure ML Experimentation

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Engage in hands-on experimentation to solidify understanding of Azure ML's capabilities and best practices.

Browse courses on Machine Learning

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Create an Azure ML workspace
Import a sample dataset and explore its features
Train a simple machine learning model using Azure ML SDK
Evaluate the model's performance and identify areas for improvement
Deploy the model as a web service

Azure ML Code Challenges

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Sharpen coding skills in Azure ML through engaging and interactive code challenges.

Browse courses on Machine Learning

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Identify online platforms or resources providing Azure ML code challenges
Select challenges that align with course topics
Solve the challenges by implementing Azure ML code
Share solutions with peers or participate in online forums

Azure ML Blog Post

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Convey knowledge and understanding of Azure ML by creating a comprehensive blog post on a chosen topic.

Browse courses on Machine Learning

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Identify a specific aspect of Azure ML to focus on
Conduct thorough research and gather relevant information
Write a well-structured and informative blog post
Share the blog post on social media or other platforms

Azure ML Solution Design

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Demonstrate proficiency in Azure ML solution design by developing a comprehensive plan.

Browse courses on Machine Learning

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Define the problem statement and business objectives
Identify and gather relevant data
Explore and select appropriate Azure ML services
Design the solution architecture and workflow
Create a detailed implementation plan

Personal Machine Learning Project

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Apply knowledge and skills gained in the course to a self-directed machine learning project.

Browse courses on Machine Learning

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Identify a problem or opportunity that can be addressed using machine learning
Gather and prepare relevant data
Develop and train a machine learning model
Evaluate the model's performance
Deploy and monitor the model

Career center

Learners who complete Build and Operate Machine Learning Solutions with Azure will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers develop, test, and deploy machine learning models. They work with data scientists to understand the business problem and translate it into a technical solution. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Machine Learning Engineer

Cloud Architect

Cloud Architects design, build, and manage cloud computing systems. They work with customers to understand their business needs and translate them into technical requirements. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Cloud Architect

Data Scientist

Data Scientists use data to solve business problems. They work with data to identify trends, patterns, and insights. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and test software applications. They work with end users to understand their needs and translate them into technical requirements. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Software Engineer

DevOps Engineer

DevOps Engineers work with developers and operations teams to ensure that software is deployed and managed efficiently. They work to automate processes and improve collaboration between teams. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for DevOps Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. They work with stakeholders to communicate insights and make recommendations. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts work with businesses to understand their needs and translate them into technical requirements. They work with stakeholders to define and manage projects. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Business Analyst

Project Manager

Project Managers plan, execute, and close projects. They work with stakeholders to define and manage projects. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Project Manager

Technical Writer

Technical Writers create and maintain documentation for software and hardware products. They work with engineers and other technical staff to understand the product and translate it into clear and concise documentation. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Technical Writer

Sales Engineer

Sales Engineers work with customers to understand their needs and sell them products and services. They work with technical staff to provide support and training. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Sales Engineer

Marketing Manager

Marketing Managers plan and execute marketing campaigns. They work with customers to understand their needs and develop marketing strategies. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Marketing Manager

Product Manager

Product Managers plan and develop products. They work with customers to understand their needs and develop product strategies. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Product Manager

Quality Assurance Analyst

Quality Assurance Analysts test software and hardware products to ensure that they meet quality standards. They work with engineers to identify and fix defects. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Quality Assurance Analyst

Systems Analyst

Systems Analysts design and implement computer systems. They work with customers to understand their needs and develop system specifications. This course can help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Systems Analyst

Database Administrator

Database Administrators manage and maintain databases. They work with engineers to design and implement database systems. This course may help you build a foundation in Azure Machine Learning, which is a cloud-based platform for training, deploying, and managing machine learning models. You will learn how to use Azure Machine Learning to create and manage enterprise-ready ML solutions.

See salaries and explore the career path for Database Administrator