MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning

Course Overview

This course provides learners with the knowledge and skills to automate and optimize their data pipelines using Microsoft Azure Machine Learning. Learners will gain an understanding of how to set up automated monitoring of their data pipelines for prediction, as well as how to monitor for data drift, model drift and feedback loops. They will also learn about setting triggers and alarms, and ethical issues in machine learning and the risks they pose.
Possible development paths for learners include becoming a data scientist, machine learning engineer, or data engineer. Learners may also pursue a career in software engineering, artificial intelligence, or cloud computing.
Learning suggestions for learners include taking courses in data science, machine learning, software engineering, artificial intelligence, and cloud computing. Learners should also consider reading books and articles on data science, machine learning, and cloud computing. Additionally, they should practice coding and building data pipelines in order to gain hands-on experience.

[Applications]
Upon completion of this course, participants should be able to apply the concepts and techniques learned to automate and optimize their data pipelines using Microsoft Azure Machine Learning. They should also be able to monitor their data pipelines for data drift, model drift, and feedback loops, as well as set triggers and alarms to address model instability. Additionally, participants should be able to apply the Responsible Data Science framework to their projects to ensure ethical considerations are taken into account.

[Career Paths]
1. MLOps Engineer: MLOps Engineers are responsible for the development, deployment, and maintenance of machine learning models. They are responsible for ensuring that the models are running efficiently and accurately, and that they are meeting the needs of the organization. MLOps Engineers must have a strong understanding of the underlying technologies and be able to troubleshoot any issues that arise. As the demand for machine learning models increases, the need for MLOps Engineers is expected to grow.

2. Data Scientist: Data Scientists are responsible for analyzing data and developing models to solve business problems. They must have a strong understanding of data analysis techniques, machine learning algorithms, and statistical methods. Data Scientists must also be able to communicate their findings to stakeholders and make recommendations based on their analysis. As the demand for data-driven insights increases, the need for Data Scientists is expected to grow.

3. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They must have a strong understanding of the underlying technologies and be able to troubleshoot any issues that arise. As the demand for machine learning models increases, the need for Machine Learning Engineers is expected to grow.

4. AI/ML Architect: AI/ML Architects are responsible for designing and implementing AI/ML solutions. They must have a strong understanding of the underlying technologies and be able to troubleshoot any issues that arise. As the demand for AI/ML solutions increases, the need for AI/ML Architects is expected to grow.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path focuses on the fundamentals of computer science, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and data science. This degree is becoming increasingly popular as the demand for data scientists and machine learning engineers grows.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence and machine learning algorithms. It covers topics such as natural language processing, computer vision, robotics, and deep learning. This degree is becoming increasingly popular as the demand for AI and ML engineers grows.

3. Master of Science in Data Science: This degree path focuses on the fundamentals of data science, including data mining, data analysis, and data visualization. It also covers topics such as machine learning, artificial intelligence, and big data. This degree is becoming increasingly popular as the demand for data scientists grows.

4. Master of Science in Machine Learning: This degree path focuses on the development of machine learning algorithms and their applications. It covers topics such as supervised and unsupervised learning, deep learning, and reinforcement learning. This degree is becoming increasingly popular as the demand for machine learning engineers grows.