Production Machine Learning Systems
This course provides an in-depth exploration of the components and best practices for successful machine learning systems in production environments. Learn how to build and maintain high-performing ML systems. ▼
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Course Feature
Cost:
Free Trial
Provider:
Pluralsight
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
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Updated in [March 06th, 2023]
This course, Production Machine Learning Systems, provides an overview of the components and best practices of a high-performing ML system in production environments. Students will learn how to design, build, and deploy ML systems that are robust, reliable, and secure. Topics covered include data engineering, model selection, model deployment, and model monitoring. By the end of the course, students will have the skills to design and deploy ML systems that are optimized for production environments.
[Applications]
The application of this course is to provide students with the knowledge and skills to build and maintain production machine learning systems. Students will learn how to design, develop, and deploy ML systems in production environments. They will also gain an understanding of the best practices for building and maintaining ML systems, as well as the challenges and considerations that come with deploying ML systems in production. Additionally, students will gain an understanding of the different components of a production ML system, such as data pipelines, model training, and model deployment. With this knowledge, students will be able to create and maintain production ML systems that are reliable, efficient, and secure.
[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models in production environments. They must have a strong understanding of the underlying algorithms and techniques used in machine learning, as well as the ability to design and implement efficient and reliable systems. The demand for Machine Learning Engineers is growing rapidly, as organizations are increasingly looking to leverage the power of machine learning to improve their products and services.
2. Data Scientist: Data Scientists are responsible for analyzing large datasets to uncover insights and trends. They must have a strong understanding of data analysis techniques, as well as the ability to interpret and communicate the results of their analyses. Data Scientists are in high demand, as organizations are increasingly looking to leverage the power of data to make better decisions.
3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI-based systems. They must have a strong understanding of the underlying algorithms and techniques used in AI, as well as the ability to design and implement efficient and reliable systems. The demand for Artificial Intelligence Engineers is growing rapidly, as organizations are increasingly looking to leverage the power of AI to improve their products and services.
4. Machine Learning Researcher: Machine Learning Researchers are responsible for researching and developing new algorithms and techniques for machine learning. They must have a strong understanding of the underlying algorithms and techniques used in machine learning, as well as the ability to design and implement efficient and reliable systems. The demand for Machine Learning Researchers is growing rapidly, as organizations are increasingly looking to leverage the power of machine learning to improve their products and services.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree program provides students with a comprehensive understanding of computer science fundamentals, including programming, software engineering, and data structures. It also covers topics such as artificial intelligence, machine learning, and robotics. With the increasing demand for AI and ML professionals, this degree path is becoming increasingly popular.
2. Master of Science in Artificial Intelligence: This degree program focuses on the development of AI and ML systems. It covers topics such as natural language processing, computer vision, and robotics. It also provides students with the skills to design and implement AI and ML systems in production environments.
3. Master of Science in Data Science: This degree program focuses on the analysis and interpretation of data. It covers topics such as data mining, machine learning, and predictive analytics. It also provides students with the skills to develop and deploy data-driven solutions in production environments.
4. Doctor of Philosophy in Machine Learning: This degree program focuses on the development of advanced ML algorithms and systems. It covers topics such as deep learning, reinforcement learning, and natural language processing. It also provides students with the skills to develop and deploy ML systems in production environments.
Pros & Cons
Good overview of designing realworld ML systems
Lots of production examples, labs and reviews
Presents all aspects of implementing a Production ML system
Provides guidance for evaluating alternatives
Poor assignments with no feedback
Too short and fast for beginners
Poorly created course with machinegenerated voices
Lack of details in labs
Issues with Qwiklab labs
Course Provider
Provider Pluralsight's Stats at AZClass
Pluralsight ranked 16th on the Best Medium Workplaces List.
Pluralsight ranked 20th on the Forbes Cloud 100 list of the top 100 private cloud companies in the world.
Pluralsight Ranked on the Best Workplaces for Women List for the second consecutive year.
AZ Class hope that this free trial Pluralsight course can help your Machine Learning skills no matter in career or in further education. Even if you are only slightly interested, you can take Production Machine Learning Systems course with confidence!
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