Machine Learning for Data Science: Machine Learning Devops
This course program provides data scientists with the opportunity to gain a comprehensive understanding of Machine Learning for Data Science through Udacity's MLOPs course. Learn the fundamentals of Machine Learning and its applications in the field of Data Science. ▼
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Course Feature
Cost:
Paid
Provider:
Udacity
Certificate:
No Information
Language:
English
Course Overview
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Updated in [March 06th, 2023]
This course provides an overview of Machine Learning for Data Science. It covers the principles of clean code, building a reproducible model workflow, deploying a scalable ML pipeline in production, and automated model scoring and monitoring. Participants will gain an understanding of the fundamentals of Machine Learning and how to apply them to their own projects. They will also learn how to develop and deploy a scalable ML pipeline in production, as well as how to automate model scoring and monitoring. By the end of the course, participants will have the skills and knowledge to apply Machine Learning to their own data science projects.
[Applications]
After taking this course, students should be able to apply the principles of clean code to their machine learning projects, build a reproducible model workflow, deploy a scalable ML pipeline in production, and automate model scoring and monitoring. Additionally, students should be able to identify and address potential issues in their ML pipelines, such as data leakage, overfitting, and bias. Finally, students should be able to use DevOps tools to automate their ML pipelines and ensure that their models are running efficiently and reliably.
[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models in production. They are responsible for building and maintaining the infrastructure that supports the models, as well as for ensuring that the models are performing as expected. They must be knowledgeable in software engineering, data engineering, and machine learning algorithms. As machine learning becomes more widely adopted, the demand for Machine Learning Engineers is expected to increase.
2. Data Scientist: Data Scientists are responsible for analyzing data and extracting insights from it. They must be knowledgeable in statistics, mathematics, and computer science. They must also be able to communicate their findings to stakeholders. As data becomes more widely available, the demand for Data Scientists is expected to increase.
3. DevOps Engineer: DevOps Engineers are responsible for automating the deployment and management of software applications. They must be knowledgeable in software engineering, system administration, and automation tools. As organizations move towards more automated and cloud-based solutions, the demand for DevOps Engineers is expected to increase.
4. Machine Learning Researcher: Machine Learning Researchers are responsible for researching and developing new machine learning algorithms and techniques. They must be knowledgeable in mathematics, computer science, and machine learning algorithms. As machine learning becomes more widely adopted, the demand for Machine Learning Researchers is expected to increase.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, and software engineering. It also covers topics such as machine learning, artificial intelligence, and data science. With the increasing demand for data-driven solutions, this degree path is becoming increasingly popular.
2. Master of Science in Artificial Intelligence: This degree path focuses on the development of intelligent systems and their applications. It covers topics such as machine learning, natural language processing, computer vision, robotics, and more. This degree path is ideal for those looking to pursue a career in the field of artificial intelligence and machine learning.
3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, predictive analytics, and more. This degree path is ideal for those looking to pursue a career in the field of data science.
4. Master of Science in Machine Learning: This degree path focuses on the development of algorithms and models for machine learning. It covers topics such as supervised and unsupervised learning, deep learning, reinforcement learning, and more. This degree path is ideal for those looking to pursue a career in the field of machine learning.
Course Provider
Provider Udacity's Stats at AZClass
Machine Learning for Data Science: Machine Learning Devops is a comprehensive course that covers the fundamentals of machine learning and its application to data science. It provides an overview of development paths and related learning suggestions to help learners understand machine learning concepts and techniques. The course focuses on clean code principles, building reproducible model workflows, deploying scalable ML pipelines in production, and automating model scoring and monitoring. Learners will gain the skills to develop and deploy ML models in production and the ability to monitor and score models in real time. This course is ideal for data scientists, ML engineers, and DevOps engineers who want to learn the fundamentals of ML and its application to data science.
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