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Mohammed Osman
Managing the machine learning process using recommended practices is a must to enable collaboration, tracing, and real-time monitoring. This course will teach you what are the main concerns and issues you need to consider while developing a machine learning model and after deploying it.
Read more
Managing the machine learning process using recommended practices is a must to enable collaboration, tracing, and real-time monitoring. This course will teach you what are the main concerns and issues you need to consider while developing a machine learning model and after deploying it.
Read more
Managing the machine learning process using recommended practices is a must to enable collaboration, tracing, and real-time monitoring. This course will teach you what are the main concerns and issues you need to consider while developing a machine learning model and after deploying it.
Machine Learning is a robust science that can empower the business with unique competitive advantages to address several challenges, such as sales price prediction, customer segment classification, and product recommendation. In this course, Demystifying Machine Learning Operations (MLOps), you’ll learn to implement machine learning operations into your machine learning project. First, you’ll explore how to apply machine learning operations (MLOps) practices for your infrastructure. Next, you’ll discover how machine learning operations (MLOps) during model development. Finally, you’ll learn how to apply machine learning operations (MLOps) after model deployment. When you’re finished with this course, you’ll have the skills and knowledge of machine learning operations needed to manage the MLOps lifecycle of your project.
Register for this course and see more details by visiting:
OpenCourser.com/course/6an1t1/demystifying
What's inside
Syllabus
Course Overview
Understanding Machine Learning Operations
Machine Learning Operations for Infrastructure
Implementing Best Practices for Model Development
Read more
Syllabus
Course Overview
Understanding Machine Learning Operations
Machine Learning Operations for Infrastructure
Implementing Best Practices for Model Development
Read more
Implementing Best Practices for Model Deployment
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches best practices for machine learning development and deployment
Covers tools and methods that align with industry standards
Helps learners manage and monitor machine learning models throughout the lifecycle
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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 Demystifying Machine Learning Operations (MLOps) with these
activities:
Review programming concepts
Show steps
Strengthen programming skills in languages commonly used for machine learning, such as Python or R, to ensure a solid foundation for implementing machine learning algorithms.
Browse courses on
Programming
Show steps
-
Revisit basic programming concepts
-
Practice writing and executing code snippets
-
Complete coding challenges or exercises
Review machine learning concepts
Show steps
Review the fundamentals of machine learning, including supervised and unsupervised learning, model selection, and evaluation.
Browse courses on
Machine Learning
Show steps
-
Read through introductory materials on machine learning
-
Solve practice problems on supervised and unsupervised learning
-
Identify and research different machine learning algorithms
Practice implementing machine learning algorithms
Show steps
Gain hands-on experience implementing various machine learning algorithms to solidify your understanding.
Show steps
-
Use online coding platforms or tutorials to practice coding machine learning algorithms.
-
Work through practice problems or assignments that require you to implement machine learning models.
-
Participate in online coding challenges or competitions to test your skills.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in study groups or discussions
Show steps
Engage with other learners to discuss concepts, share knowledge, and resolve challenges, fostering collaboration and improving comprehension.
Browse courses on
Machine Learning
Show steps
-
Join or form a study group with peers
-
Actively participate in discussions and ask questions
-
Share knowledge and insights with others
Practice building machine learning models
Show steps
Apply machine learning techniques to real-world datasets to gain hands-on experience and improve model-building skills.
Browse courses on
Model Building
Show steps
-
Choose a dataset and define the problem statement
-
Explore and preprocess the data
-
Train and evaluate different machine learning models
-
Fine-tune and optimize the best performing model
Write a blog post or article on machine learning
Show steps
Summarize key concepts, share practical insights, or discuss current trends in machine learning to reinforce understanding and improve communication skills.
Browse courses on
Machine Learning
Show steps
-
Choose a specific topic within machine learning
-
Research and gather information on the topic
-
Write a well-structured and informative blog post or article
-
Share the content on relevant platforms
Explore advanced machine learning techniques
Show steps
Delve into specialized machine learning areas such as deep learning, natural language processing, or time series analysis to expand knowledge and skills.
Browse courses on
Advanced Machine Learning
Show steps
-
Identify an area of interest within advanced machine learning
-
Find online tutorials or courses on the chosen topic
-
Complete the tutorials and practice exercises
Build a machine learning project
Show steps
Apply machine learning concepts to solve a real-world problem, gain hands-on experience, and develop a comprehensive project portfolio.
Show steps
-
Define the project scope and objectives
-
Gather and preprocess data
-
Build, train, and evaluate machine learning models
-
Deploy the model and track its performance
-
Document and present the project outcomes
Review programming concepts
Show steps
Strengthen programming skills in languages commonly used for machine learning, such as Python or R, to ensure a solid foundation for implementing machine learning algorithms.
Browse courses on
Programming
Show steps
Show steps
Show steps
- Revisit basic programming concepts
- Practice writing and executing code snippets
- Complete coding challenges or exercises
Review machine learning concepts
Show steps
Review the fundamentals of machine learning, including supervised and unsupervised learning, model selection, and evaluation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read through introductory materials on machine learning
- Solve practice problems on supervised and unsupervised learning
- Identify and research different machine learning algorithms
Practice implementing machine learning algorithms
Show steps
Gain hands-on experience implementing various machine learning algorithms to solidify your understanding.
Show steps
Show steps
Show steps
- Use online coding platforms or tutorials to practice coding machine learning algorithms.
- Work through practice problems or assignments that require you to implement machine learning models.
- Participate in online coding challenges or competitions to test your skills.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in study groups or discussions
Show steps
Engage with other learners to discuss concepts, share knowledge, and resolve challenges, fostering collaboration and improving comprehension.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join or form a study group with peers
- Actively participate in discussions and ask questions
- Share knowledge and insights with others
Practice building machine learning models
Show steps
Apply machine learning techniques to real-world datasets to gain hands-on experience and improve model-building skills.
Browse courses on
Model Building
Show steps
Show steps
Show steps
- Choose a dataset and define the problem statement
- Explore and preprocess the data
- Train and evaluate different machine learning models
- Fine-tune and optimize the best performing model
Write a blog post or article on machine learning
Show steps
Summarize key concepts, share practical insights, or discuss current trends in machine learning to reinforce understanding and improve communication skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a specific topic within machine learning
- Research and gather information on the topic
- Write a well-structured and informative blog post or article
- Share the content on relevant platforms
Explore advanced machine learning techniques
Show steps
Delve into specialized machine learning areas such as deep learning, natural language processing, or time series analysis to expand knowledge and skills.
Browse courses on
Advanced Machine Learning
Show steps
Show steps
Show steps
- Identify an area of interest within advanced machine learning
- Find online tutorials or courses on the chosen topic
- Complete the tutorials and practice exercises
Build a machine learning project
Show steps
Apply machine learning concepts to solve a real-world problem, gain hands-on experience, and develop a comprehensive project portfolio.
Show steps
Show steps
Show steps
- Define the project scope and objectives
- Gather and preprocess data
- Build, train, and evaluate machine learning models
- Deploy the model and track its performance
- Document and present the project outcomes
Career center
Learners who complete Demystifying Machine Learning Operations (MLOps) will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work with data scientists and other engineers to ensure that models are accurate and efficient. This course will help you build a strong foundation in MLOps, which is essential for success as a Machine Learning Engineer. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your models are reliable and performant.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical techniques to extract insights from data. They work with businesses to identify opportunities and solve problems. This course will help you build a foundation in MLOps, which is essential for success as a Data Scientist. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your models are accurate and reliable.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with other engineers and stakeholders to ensure that software systems meet the needs of users. This course may be useful for Software Engineers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your software systems are reliable and performant.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. They work with businesses to identify opportunities to use cloud computing to improve efficiency and reduce costs. This course may be useful for Cloud Architects who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your cloud computing systems are reliable and performant.
DevOps Engineer
DevOps Engineer
DevOps Engineers work with development and operations teams to ensure that software systems are deployed and maintained efficiently. They use automation and other tools to streamline the software development process. This course may be useful for DevOps Engineers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your software systems are reliable and performant.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. They work with businesses to improve decision-making and solve problems. This course may be useful for Data Analysts who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your data analysis is accurate and reliable.
Project Manager
Project Manager
Project Managers plan and execute projects. They work with teams to ensure that projects are completed on time, within budget, and to the required quality. This course may be useful for Project Managers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your projects are successful.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify opportunities and solve problems. They use data analysis and other techniques to help businesses make better decisions. This course may be useful for Business Analysts who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your business analysis is accurate and reliable.
Product Manager
Product Manager
Product Managers work with teams to develop and launch new products. They are responsible for defining the product vision, roadmap, and features. This course may be useful for Product Managers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your products are successful.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They work with engineers and other stakeholders to ensure that documentation is accurate and easy to understand. This course may be useful for Technical Writers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your documentation is accurate and reliable.
IT Manager
IT Manager
IT Managers are responsible for planning, implementing, and managing IT systems. They work with businesses to identify opportunities to use technology to improve efficiency and reduce costs. This course may be useful for IT Managers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your IT systems are reliable and performant.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test software and other products to ensure that they meet quality standards. They work with development teams to identify and fix defects. This course may be useful for Quality Assurance Analysts who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your software products are reliable and performant.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines. They work with data scientists and other engineers to ensure that data is available and usable for analysis. This course may be useful for Data Engineers who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your data pipelines are reliable and performant.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. They work with businesses to ensure that databases are reliable and performant. This course may be useful for Database Administrators who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your databases are reliable and performant.
Systems Administrator
Systems Administrator
Systems Administrators are responsible for managing and maintaining computer systems. They work with businesses to ensure that computer systems are reliable and performant. This course may be useful for Systems Administrators who want to learn more about MLOps. You will learn how to manage the MLOps lifecycle, from infrastructure setup to model deployment. You will also learn how to implement best practices for model development and deployment, which will help you to ensure that your computer systems are reliable and performant.
For more career information including salaries, visit:
OpenCourser.com/course/6an1t1/demystifying
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
Demystifying Machine Learning Operations (MLOps).
Provides a comprehensive overview of machine learning systems, covering topics such as model selection, training, and evaluation. It valuable resource for learners looking to gain a deeper understanding of the theoretical foundations of machine learning.
Provides valuable background knowledge on designing and building data-intensive applications. It covers topics such as data modeling, storage, and processing, which are essential for understanding the infrastructure aspects of MLOps.
Save
Provides a comprehensive overview of deep learning concepts and algorithms. It valuable resource for learners looking to gain a strong foundation in deep learning, which is an essential component of many modern machine learning models.
Provides a comprehensive overview of machine learning techniques, including supervised and unsupervised learning. It valuable reference for learners looking to gain a broader understanding of the theoretical foundations of machine learning.
Provides a hands-on introduction to machine learning using Python. It valuable resource for learners who are new to machine learning and looking to gain practical experience in applying machine learning techniques to real-world problems.
Provides a comprehensive overview of artificial intelligence concepts and techniques. It valuable resource for learners looking to gain a broader understanding of the theoretical foundations of artificial intelligence, which is the broader field that encompasses machine learning.
Provides a comprehensive overview of sparse modeling techniques, which are commonly used in machine learning for feature selection and model regularization. It valuable reference for learners looking to gain a deeper understanding of these techniques.
Provides a practical introduction to deep learning using the fastai library. It valuable resource for learners who are new to deep learning and looking for a hands-on approach using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6an1t1/demystifying
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Time
134 hours
Level
Intermediate
Via
Pluralsight
Instructor
Mohammed Osman
Language
English
Good to know
Teaches best practices for machine learning development and deployment
Covers tools and methods that align with industry standards
Helps learners manage and monitor machine learning models throughout the lifecycle
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