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Sahil Malik
This course shows you how to manage AI solutions in Azure. It explains how you can use ML Ops, monitor and collect data in production AKS clusters, and automate the entire process end to end
Companies and Governments across the globe are pouring billions of dollars into AI. The projects are getting ever more interesting and complex, and it is therefore natural to conclude that these projects need management.
Read more
This course shows you how to manage AI solutions in Azure. It explains how you can use ML Ops, monitor and collect data in production AKS clusters, and automate the entire process end to end
Companies and Governments across the globe are pouring billions of dollars into AI. The projects are getting ever more interesting and complex, and it is therefore natural to conclude that these projects need management.
Read more
This course shows you how to manage AI solutions in Azure. It explains how you can use ML Ops, monitor and collect data in production AKS clusters, and automate the entire process end to end
Companies and Governments across the globe are pouring billions of dollars into AI. The projects are getting ever more interesting and complex, and it is therefore natural to conclude that these projects need management.
In this course, Managing Microsoft Azure AI Solutions, I assert that an AI project is like any other software project, and the need to manage it with good software practices is more, not less, important. With demos, you'll learn how you can use concepts such as Azure CLI, ML SDK, and ML Ops to fully automate your end to end process. You'll also explore how you can set up an Azure DevOps pipeline to go from experiment to a service. But the fun doesn't end there; you'll then discover how to deploy your model as an AKS cluster and enable data monitoring and collection in production, so you can use that data in numerous ways to analyze it or feed it back into your model for subsequent improvement.
By the end of this course, you'll have an in-depth understanding of how to manage your AI projects like a proper software project. Concepts such as ML Ops and Pipelines will be second nature to you, and you'll be a pro at collecting and monitoring your production AI solutions.
This course is no longer available. Find something similar by browsing:
Azure CLI
ML SDK
ML Ops
Azure DevOps
AKS
Data Monitoring
Data Collection
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What's inside
Syllabus
Course Overview
Managing Models in Azure Machine Learning Service
Registering Your Model
Registering and Deploying Your Image
Read more
Syllabus
Course Overview
Managing Models in Azure Machine Learning Service
Registering Your Model
Registering and Deploying Your Image
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides foundational knowledge on AI project management, including important concepts like ML Ops and pipelines
Emphasizes the importance of managing AI projects like any other software project, highlighting the significance of good software practices
Involves hands-on demonstrations and real-world examples, making the learning process more engaging and practical
Covers a range of topics, from registering and deploying models to monitoring and improving data and models, providing a comprehensive understanding of AI project management
Facilitates the deployment of models as AKS clusters, enabling data monitoring and collection in production for further analysis and model improvement
Instructor Sahil Malik is recognized for their expertise in AI and ML
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Reviews summary
Practical azure ai solution management
According to students, this course offers a highly practical and hands-on approach to managing AI solutions in Azure. Learners particularly praise its focus on MLOps concepts and the end-to-end automation of AI pipelines using Azure CLI, ML SDK, and Azure DevOps. The demonstrations are frequently highlighted as useful for understanding deployment to AKS clusters and setting up data monitoring and collection in production. While the course is seen as a solid foundation, some learners suggest a prerequisite understanding of Azure fundamentals and Python scripting would enhance the learning experience. Overall, it's considered highly relevant for professionals looking to operationalize AI.
Instructor's delivery aids understanding of complex topics effectively.
"The instructor did a fantastic job explaining complex topics like AKS deployment in an understandable way."
"Pacing was just right, allowing me to absorb the information without feeling rushed."
"I found the lectures well-structured and easy to follow, even for advanced concepts."
Material is current and relevant for modern Azure AI practices.
"I was impressed that the course material reflected the latest Azure updates for AI services."
"The instructor regularly updates the labs, which is crucial for a fast-changing platform like Azure."
"Unlike other courses, this one felt very current and applicable to today's cloud environment."
Covers the full lifecycle of AI solution management comprehensively.
"This course provided a thorough understanding of the entire MLOps process from experiment to production."
"I now have a clear picture of how to use Azure DevOps for continuous deployment of AI models."
"It really helped in understanding how to manage AI projects like proper software projects."
Offers valuable hands-on experience for MLOps implementation.
"The hands-on coding and projects are the strongest part of the course for me, helping to solidify complex concepts."
"I appreciated the practical demonstrations that showed exactly how to implement ML Ops in Azure."
"The labs were very useful; I could immediately apply what I learned to my work projects."
"The demos truly clarify how to deploy models as AKS clusters and set up data monitoring."
Some learners encountered challenges with initial lab configuration.
"Setting up the lab environment was a bit tricky and took a considerable amount of time."
"I ran into a few issues with specific Azure resource limitations during the exercises."
"While the labs are great, the initial configuration steps could be streamlined or provided with clearer troubleshooting."
Best suited for learners with existing Azure and Python background.
"Learners should have a solid understanding of Azure fundamentals before attempting this course, as it moves quite fast."
"I wish there was a clearer statement about the need for strong Python scripting skills to fully benefit."
"While excellent, this course is definitely for those already comfortable with cloud concepts and some machine learning basics."
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 Managing Microsoft Azure AI Solutions with these
activities:
Organize and Review Course Materials
Show steps
Enhance your comprehension and retention by organizing and reviewing course materials regularly.
Show steps
-
Gather and organize lecture notes, assignments, quizzes, and exams.
-
Review the materials to reinforce your understanding.
Review Azure CLI Basic Commands
Show steps
Build a stronger foundation for managing Azure AI solutions by reviewing the basics of Azure CLI.
Browse courses on
Command Line Interface
Show steps
-
Go through the Microsoft documentation for Azure CLI basics.
-
Practice using the Azure CLI in a sandbox or local environment.
Hands-on Practice with ML SDK
Show steps
Reinforce your understanding of ML SDK by engaging in practical exercises.
Show steps
-
Find tutorials or documentation that provide hands-on exercises with ML SDK.
-
Set up a development environment and follow the instructions to complete the exercises.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Azure DevOps Pipeline
Show steps
Deepen your understanding of Azure DevOps by following guided tutorials.
Browse courses on
Azure DevOps
Show steps
-
Search for tutorials or documentation that provide a step-by-step guide to Azure DevOps Pipeline.
-
Follow the instructions in the tutorial to set up and use Azure DevOps Pipeline.
Develop an ML Ops Pipeline
Show steps
Demonstrate your mastery of ML Ops by creating a complete pipeline.
Show steps
-
Plan and design the ML Ops pipeline.
-
Implement the pipeline using Azure tools and services.
-
Test and evaluate the pipeline's performance.
Gather Resources on Data Monitoring and Collection in Production
Show steps
Expand your knowledge by compiling resources on data monitoring and collection in production.
Browse courses on
Data Monitoring
Show steps
-
Search for articles, whitepapers, and documentation on data monitoring and collection.
-
Organize and curate the gathered resources into a central location.
Seek Guidance from Azure AI Experts
Show steps
Enhance your understanding by connecting with experienced professionals in the field.
Browse courses on
Mentorship
Show steps
-
Identify potential mentors who have expertise in Azure AI.
-
Reach out to mentors and schedule meetings or discussions to seek guidance.
Contribute to Azure AI Open Source Projects
Show steps
Gain practical experience and expand your network by contributing to open source projects related to Azure AI.
Browse courses on
Open Source
Show steps
-
Identify open source projects aligned with your interests and skills.
-
Review the project documentation and identify areas where you can contribute.
-
Make code contributions, report bugs, or participate in discussions.
Organize and Review Course Materials
Show steps
Enhance your comprehension and retention by organizing and reviewing course materials regularly.
Show steps
Show steps
Show steps
- Gather and organize lecture notes, assignments, quizzes, and exams.
- Review the materials to reinforce your understanding.
Review Azure CLI Basic Commands
Show steps
Build a stronger foundation for managing Azure AI solutions by reviewing the basics of Azure CLI.
Browse courses on
Command Line Interface
Show steps
Show steps
Show steps
- Go through the Microsoft documentation for Azure CLI basics.
- Practice using the Azure CLI in a sandbox or local environment.
Hands-on Practice with ML SDK
Show steps
Reinforce your understanding of ML SDK by engaging in practical exercises.
Show steps
Show steps
Show steps
- Find tutorials or documentation that provide hands-on exercises with ML SDK.
- Set up a development environment and follow the instructions to complete the exercises.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Azure DevOps Pipeline
Show steps
Deepen your understanding of Azure DevOps by following guided tutorials.
Browse courses on
Azure DevOps
Show steps
Show steps
Show steps
- Search for tutorials or documentation that provide a step-by-step guide to Azure DevOps Pipeline.
- Follow the instructions in the tutorial to set up and use Azure DevOps Pipeline.
Develop an ML Ops Pipeline
Show steps
Demonstrate your mastery of ML Ops by creating a complete pipeline.
Show steps
Show steps
Show steps
- Plan and design the ML Ops pipeline.
- Implement the pipeline using Azure tools and services.
- Test and evaluate the pipeline's performance.
Gather Resources on Data Monitoring and Collection in Production
Show steps
Expand your knowledge by compiling resources on data monitoring and collection in production.
Browse courses on
Data Monitoring
Show steps
Show steps
Show steps
- Search for articles, whitepapers, and documentation on data monitoring and collection.
- Organize and curate the gathered resources into a central location.
Seek Guidance from Azure AI Experts
Show steps
Enhance your understanding by connecting with experienced professionals in the field.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify potential mentors who have expertise in Azure AI.
- Reach out to mentors and schedule meetings or discussions to seek guidance.
Contribute to Azure AI Open Source Projects
Show steps
Gain practical experience and expand your network by contributing to open source projects related to Azure AI.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open source projects aligned with your interests and skills.
- Review the project documentation and identify areas where you can contribute.
- Make code contributions, report bugs, or participate in discussions.
Career center
Learners who complete Managing Microsoft Azure AI Solutions will develop knowledge and skills
that may be useful to these careers:
Data Science Manager
Data Science Manager
Data Science Managers lead teams of data scientists and engineers in the development and deployment of AI solutions. This course will teach you how to manage AI solutions in Azure, making you a more effective Data Science Manager.
AI Engineer
AI Engineer
AI Engineers design, develop, test, deploy, and manage AI systems. This course, Managing Microsoft Azure AI Solutions, can help aspiring AI Engineers learn how to manage AI solutions in Azure, including how to use concepts like ML Ops, pipelines, and data monitoring and collection in production AKS clusters.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, test, deploy, maintain, and manage machine learning systems. This course, Managing Microsoft Azure AI Solutions, will teach you how to manage AI solutions in Azure, including end to end automation, ML Ops, and data monitoring and collection in production AKS clusters.
Cloud Architect
Cloud Architect
Cloud Architects design, build, and manage cloud computing systems. This course will teach you how to manage AI solutions in Azure, making you a more effective Cloud Architect in the growing field of AI.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure and tools that are used to store, process, and analyze data. Managing Microsoft Azure AI Solutions can be useful for aspiring Data Engineers as it teaches how to use concepts like ML Ops and Azure DevOps pipelines to go from experiment to a service.
DevOps Engineer
DevOps Engineer
DevOps Engineers integrate development and operations teams to improve the efficiency and quality of software development and delivery. Managing Microsoft Azure AI Solutions can be useful for aspiring DevOps Engineers as it teaches how to use Azure DevOps pipelines to go from experiment to a service.
Solutions Architect
Solutions Architect
Solutions Architects design, build, and manage complex technology solutions. This course will teach you how to manage AI solutions in Azure, making you a more effective Solutions Architect in the growing field of AI.
Data Scientist
Data Scientist
Data Scientists use their expertise in mathematics, statistics, and programming to collect, analyze, interpret, and present complex data. With the skills learned in Managing Microsoft Azure AI Solutions, you'll be able to manage the full ML lifecycle of your AI projects, including monitoring, improving, and collecting data, as well as deploying your models as an AKS cluster. This course may be useful for aspiring Data Scientists.
Data Analyst
Data Analyst
Data Analysts collect, process, and analyze data from a variety of sources to identify trends, patterns, and other useful information. Managing Microsoft Azure AI Solutions can be useful for aspiring Data Analysts as it teaches how to use concepts like Azure CLI, ML SDK, and ML Ops to fully automate an end to end process.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to identify trends and patterns, and to make recommendations to improve business performance. This course may be helpful for aspiring Business Intelligence Analysts as it teaches how to use concepts like Azure CLI, ML SDK, and ML Ops to fully automate an end to end process.
Product Manager
Product Manager
Product Managers are responsible for the vision, strategy, and execution of products. Managing Microsoft Azure AI Solutions may be useful for aspiring Product Managers as it teaches how to manage the full ML lifecycle of AI projects, including monitoring, improving, and collecting data.
Software Engineer
Software Engineer
Software Engineers apply engineering principles to the design, development, implementation, testing, and maintenance of software systems. Managing Microsoft Azure AI Solutions will help you learn how to manage AI solutions in Azure, making you a more effective software engineer in the growing field of AI.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including AI. Managing Microsoft Azure AI Solutions may be useful for aspiring Research Scientists as it teaches how to manage the full ML lifecycle of AI projects, including monitoring, improving, and collecting data.
Consultant
Consultant
Consultants provide advice and guidance to businesses on a variety of topics, including AI. Managing Microsoft Azure AI Solutions may be useful for aspiring Consultants as it teaches how to manage the full ML lifecycle of AI projects, including monitoring, improving, and collecting data.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. Managing Microsoft Azure AI Solutions may be useful for aspiring Entrepreneurs as it teaches how to manage the full ML lifecycle of AI projects, including monitoring, improving, and collecting data. This course also provides a foundation in Azure DevOps pipelines, which can help you automate the development and deployment of your AI solutions. However, this course focuses on Azure, so it may be less useful if you are planning to build an AI solution on another platform.
For more career information including salaries, visit:
OpenCourser.com/course/khrb2k/managing
Reading list
We've selected 11 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
Managing Microsoft Azure AI Solutions.
Provides a comprehensive overview of the machine learning engineering process, from data collection and preparation to model deployment and monitoring, specially focusing on using Python.
Provides a comprehensive guide to using Kubernetes for deploying and serving machine learning models. It covers topics such as containerization, resource management, and monitoring, and offers practical examples of how to build and deploy ML applications on Kubernetes.
Provides a gentle introduction to machine learning with R programming and is helpful for beginners.
Provides a simple and clear introduction to machine learning concepts.
Provides a practical guide to machine learning using Python. It covers a wide range of topics, including data preparation, model training, and evaluation, and offers hands-on exercises and projects to help readers apply their knowledge.
Provides a comprehensive guide to machine learning using R. It covers a wide range of topics, including data preparation, model training, and evaluation, and offers practical examples of how to implement machine learning solutions using R.
Provides a practical guide to artificial intelligence using Python. It covers a wide range of topics, including machine learning, natural language processing, and computer vision, and offers hands-on exercises and projects to help readers apply their knowledge.
Provides a hands-on introduction to deep learning using Python. It covers the basics of deep learning algorithms, model architectures, and training techniques, and offers practical examples of how to implement deep learning solutions using TensorFlow and Keras.
Provides an introduction to reinforcement learning, a type of machine learning that involves learning from trial and error. It covers the basics of reinforcement learning algorithms, environments, and applications, and offers practical examples of how to implement reinforcement learning solutions.
Save
Provides a comprehensive guide to data mining, a process of extracting knowledge from large datasets. It covers a wide range of data mining techniques, including clustering, classification, and association rule mining, and offers practical examples of how to apply these techniques to real-world problems.
Provides a theoretical perspective on machine learning, focusing on Bayesian inference and optimization techniques. It covers a wide range of topics, including probabilistic graphical models, Bayesian inference, and optimization algorithms, and offers theoretical insights into the foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/khrb2k/managing
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Time
99 hours
Level
Intermediate
Via
Pluralsight
Instructor
Sahil Malik
Language
English
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