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Ever wondered what it would be like to work in machine learning operations (MLOps)? In this course, you will! You will tackle real-world scenarios through lessons and labs. You’ll learn how to ingest and prepare data, apply algorithms, score, and evaluate the data. Finally, you will tackle options for publishing, as well as troubleshoot other common issues that you might encounter along the way. The best news? This is an entry-level course, so no prior experience in machine learning or operations is necessary.
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OpenCourser.com/course/tuc7og/introduction
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Introduces the field of machine learning operations (MLOps)
Provides real-world scenarios to make learning practical and engaging
Builds a strong foundation in data ingestion, preparation, modeling, and evaluation
Covers options for publishing and troubleshooting common issues, making it well-rounded
Designed for beginners, making it accessible to those new to machine learning or operations
Taught by experienced instructors from A Cloud Guru, a reputable provider
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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 Introduction to MLOps on Azure with these
activities:
Brush up on your Python programming skills
Show steps
Ensuring your Python skills are up-to-date will make it easier to follow along with the course and complete assignments.
Browse courses on
Python Programming
Show steps
-
Review your notes or take practice exercises on Python syntax and data structures
-
Go through online tutorials or videos on Python programming
Review key concepts from probability and statistics
Show steps
Strengthen your understanding of foundational concepts in probability and statistics to enhance your grasp of machine learning algorithms.
Browse courses on
Probability
Show steps
-
Review probability distributions and statistical inference
-
Practice solving probability and statistics problems
Explore Machine Learning concepts
Show steps
Refresh your knowledge of machine learning fundamentals to ensure a solid understanding of the course material.
Browse courses on
Machine Learning
Show steps
-
Review supervised and unsupervised learning algorithms
-
Practice model evaluation and selection
-
Explore different machine learning libraries and frameworks
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Join a study group or online community
Show steps
Engaging with peers can provide diverse perspectives, clarify concepts, and boost motivation.
Show steps
-
Identify online forums or social media groups related to machine learning
-
Join the groups and actively participate in discussions
-
Connect with other learners and form study groups to collaborate on projects and assignments
Follow tutorials on data preprocessing techniques
Show steps
Enhance your data wrangling skills by following guided tutorials that cover essential preprocessing methods.
Browse courses on
Data Preprocessing
Show steps
-
Explore data cleaning and transformation techniques
-
Practice handling missing values and outliers
Walk through machine learning algorithms
Show steps
Getting hands-on experience with machine learning algorithms will deepen your understanding and solidify your skills.
Browse courses on
Machine Learning Algorithms
Show steps
-
Find tutorials or online courses that provide step-by-step instructions on implementing different machine learning algorithms
-
Follow the tutorials and implement the algorithms yourself
-
Test your understanding by applying the algorithms to real-world datasets
Participate in online forums and discussion groups
Show steps
Engage with your peers, ask questions, and share knowledge to enhance your learning and build a support network.
Show steps
-
Join online forums and discussion groups
-
Actively participate in discussions and ask for help
-
Share your knowledge and experiences with others
Solve exercises on model training and evaluation
Show steps
Reinforce your understanding of model training and evaluation through regular practice exercises.
Browse courses on
Model Training
Show steps
-
Implement different model training algorithms
-
Evaluate model performance using various metrics
-
Fine-tune models for optimal results
Practice data cleaning and preparation
Show steps
Regular practice with data cleaning and preparation tasks will improve your proficiency and efficiency.
Browse courses on
Data Cleaning
Show steps
-
Find datasets that require cleaning and preparation
-
Apply data cleaning techniques to remove errors, duplicates, and inconsistencies
-
Explore different data preparation methods to transform and normalize data
Connect with experienced MLOps professionals
Show steps
Seek guidance and support by connecting with experienced professionals in the field of MLOps.
Browse courses on
Mentorship
Show steps
-
Attend industry events and meetups
-
Reach out to potential mentors through LinkedIn or professional networks
Contribute to open-source MLOps projects
Show steps
Gain practical experience and contribute to the MLOps community by participating in open-source projects.
Browse courses on
Open Source
Show steps
-
Identify beginner-friendly projects to contribute to
-
Read the project documentation and understand its goals
-
Make code contributions and participate in discussions
Build a machine learning model for a real-world problem
Show steps
Applying your knowledge to a real-world project will provide valuable experience and showcase your skills.
Show steps
-
Identify a problem or challenge that can be addressed using machine learning
-
Gather and prepare the necessary data
-
Choose and implement appropriate machine learning algorithms
-
Evaluate the performance of your model and make necessary adjustments
-
Write a report or presentation summarizing your findings
Develop a machine learning project portfolio
Show steps
Showcase your MLOps skills by creating and maintaining a portfolio of machine learning projects.
Browse courses on
Machine Learning Projects
Show steps
-
Identify suitable projects for your skill level
-
Plan and execute projects using MLOps best practices
-
Document your projects and share them online
Brush up on your Python programming skills
Show steps
Ensuring your Python skills are up-to-date will make it easier to follow along with the course and complete assignments.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review your notes or take practice exercises on Python syntax and data structures
- Go through online tutorials or videos on Python programming
Review key concepts from probability and statistics
Show steps
Strengthen your understanding of foundational concepts in probability and statistics to enhance your grasp of machine learning algorithms.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability distributions and statistical inference
- Practice solving probability and statistics problems
Explore Machine Learning concepts
Show steps
Refresh your knowledge of machine learning fundamentals to ensure a solid understanding of the course material.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review supervised and unsupervised learning algorithms
- Practice model evaluation and selection
- Explore different machine learning libraries and frameworks
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Join a study group or online community
Show steps
Engaging with peers can provide diverse perspectives, clarify concepts, and boost motivation.
Show steps
Show steps
Show steps
- Identify online forums or social media groups related to machine learning
- Join the groups and actively participate in discussions
- Connect with other learners and form study groups to collaborate on projects and assignments
Follow tutorials on data preprocessing techniques
Show steps
Enhance your data wrangling skills by following guided tutorials that cover essential preprocessing methods.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Explore data cleaning and transformation techniques
- Practice handling missing values and outliers
Walk through machine learning algorithms
Show steps
Getting hands-on experience with machine learning algorithms will deepen your understanding and solidify your skills.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Find tutorials or online courses that provide step-by-step instructions on implementing different machine learning algorithms
- Follow the tutorials and implement the algorithms yourself
- Test your understanding by applying the algorithms to real-world datasets
Participate in online forums and discussion groups
Show steps
Engage with your peers, ask questions, and share knowledge to enhance your learning and build a support network.
Show steps
Show steps
Show steps
- Join online forums and discussion groups
- Actively participate in discussions and ask for help
- Share your knowledge and experiences with others
Solve exercises on model training and evaluation
Show steps
Reinforce your understanding of model training and evaluation through regular practice exercises.
Browse courses on
Model Training
Show steps
Show steps
Show steps
- Implement different model training algorithms
- Evaluate model performance using various metrics
- Fine-tune models for optimal results
Practice data cleaning and preparation
Show steps
Regular practice with data cleaning and preparation tasks will improve your proficiency and efficiency.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find datasets that require cleaning and preparation
- Apply data cleaning techniques to remove errors, duplicates, and inconsistencies
- Explore different data preparation methods to transform and normalize data
Connect with experienced MLOps professionals
Show steps
Seek guidance and support by connecting with experienced professionals in the field of MLOps.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Attend industry events and meetups
- Reach out to potential mentors through LinkedIn or professional networks
Contribute to open-source MLOps projects
Show steps
Gain practical experience and contribute to the MLOps community by participating in open-source projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify beginner-friendly projects to contribute to
- Read the project documentation and understand its goals
- Make code contributions and participate in discussions
Build a machine learning model for a real-world problem
Show steps
Applying your knowledge to a real-world project will provide valuable experience and showcase your skills.
Show steps
Show steps
Show steps
- Identify a problem or challenge that can be addressed using machine learning
- Gather and prepare the necessary data
- Choose and implement appropriate machine learning algorithms
- Evaluate the performance of your model and make necessary adjustments
- Write a report or presentation summarizing your findings
Develop a machine learning project portfolio
Show steps
Showcase your MLOps skills by creating and maintaining a portfolio of machine learning projects.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Identify suitable projects for your skill level
- Plan and execute projects using MLOps best practices
- Document your projects and share them online
Career center
Learners who complete Introduction to MLOps on Azure will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, deploys, and maintains machine learning models. These models can be used for a variety of purposes, such as fraud detection, customer segmentation, and predictive analytics. This course can be useful as it provides hands-on experience with the entire MLOps lifecycle, including data preparation, model training, and deployment.
Data Scientist
Data Scientist
A Data Scientist combines programming skills with knowledge of mathematics and statistics to extract meaningful insights from data using machine learning, deep learning, and artificial intelligence. This course on Azure MLOps can be useful as it provides a real-world perspective on how to apply these principles to solve business problems.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to identify trends and patterns. This information can be used to make better decisions about everything from marketing campaigns to product development. This course can be useful as it provides a foundation in data preparation and analysis, which are essential skills for Data Analysts.
Business Analyst
Business Analyst
A Business Analyst works with stakeholders to understand their business needs and then develops solutions to meet those needs. This course can be useful as it provides a foundation in business analysis principles and practices, which are essential for Business Analysts.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course can be useful as it provides a foundation in software development principles and practices, which are essential for Software Engineers.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course can be useful as it provides a foundation in product management principles and practices, which are essential for Product Managers.
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. This course can be useful as it provides a foundation in project management principles and practices, which are essential for Project Managers.
Human Resources Manager
Human Resources Manager
A Human Resources Manager is responsible for the management of human resources within a company. This course can be useful as it provides a foundation in human resources management principles and practices, which are essential for Human Resources Managers.
Teacher
Teacher
A Teacher educates students in a variety of subjects. This course can be useful as it provides a foundation in teaching principles and practices, which are essential for Teachers.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and executing marketing campaigns. This course can be useful as it provides a foundation in marketing principles and practices, which are essential for Marketing Managers.
Sales Manager
Sales Manager
A Sales Manager is responsible for leading and managing a sales team. This course can be useful as it provides a foundation in sales principles and practices, which are essential for Sales Managers.
Financial Analyst
Financial Analyst
A Financial Analyst provides insights into the financial performance of a company. This course can be useful as it provides a foundation in financial analysis principles and practices, which are essential for Financial Analysts.
Operations Manager
Operations Manager
An Operations Manager is responsible for the day-to-day operations of a business. This course can be useful as it provides a foundation in operations management principles and practices, which are essential for Operations Managers.
Consultant
Consultant
A Consultant provides advice and guidance to clients on a variety of topics. This course can be useful as it provides a foundation in consulting principles and practices, which are essential for Consultants.
Entrepreneur
Entrepreneur
An Entrepreneur starts and runs their own business. This course can be useful as it provides a foundation in entrepreneurship principles and practices, which are essential for Entrepreneurs.
For more career information including salaries, visit:
OpenCourser.com/course/tuc7og/introduction
Reading list
We've selected eight 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
Introduction to MLOps on Azure.
Provides a comprehensive overview of machine learning from a Bayesian and optimization perspective. It valuable resource for anyone looking to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of probabilistic graphical models, covering the theory and algorithms behind these models. It valuable resource for anyone looking to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, from linear regression to tree-based methods. It valuable resource for anyone looking to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive introduction to data science from scratch. It covers a wide range of topics, from data collection to model evaluation. It valuable resource for anyone looking to get started with data science.
Provides a hands-on introduction to machine learning for data science. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone looking to get started with machine learning.
Provides a collection of best practices for machine learning engineering, covering topics such as data engineering, model training, and model deployment. It valuable resource for anyone looking to improve the quality of their machine learning projects.
Save
Provides a comprehensive guide to using Azure DevOps for machine learning, covering the entire lifecycle of machine learning models from development to deployment. It valuable resource for anyone looking to gain a deeper understanding of MLOps in Azure DevOps.
Provides a practical guide to machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone looking to get started with machine learning in Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tuc7og/introduction
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Time
255 hours
Level
Intermediate
Via
Pluralsight
Instructor
A Cloud Guru
Language
English
Good to know
Introduces the field of machine learning operations (MLOps)
Provides real-world scenarios to make learning practical and engaging
Builds a strong foundation in data ingestion, preparation, modeling, and evaluation
Covers options for publishing and troubleshooting common issues, making it well-rounded
Designed for beginners, making it accessible to those new to machine learning or operations
Taught by experienced instructors from A Cloud Guru, a reputable provider
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