About this Course

This course provides an in-depth look into the realm of Machine Learning Engineering. It focuses on equipping participants with the skills to create intelligent, scalable machine learning applications integrated with modern software engineering principles and continuous delivery pipelines. The training extensively covers the foundations of AutoML technologies, including practical labs on popular tools like Ludwig and Cloud AutoML. Furthermore, the course delves into current industry trends like MLOps, edge machine learning, and AI APIs, preparing students for advanced endeavors in technology-enabled environments.

What Students Will Learn

• Best practices in machine learning engineering.
• How to build and deploy machine learning applications.
• Use of continuous delivery pipelines in machine learning workflows.
• Understanding and applying AutoML for efficient model training.
• Skills in managing machine learning operations (MLOps).
• Knowledge of emerging technologies like edge machine learning and AI APIs.

Prerequisites

No prerequisites are required for this course, making it accessible for beginners with a basic understanding of computer technology and a keen interest in machine learning.

Course Coverage

• Principles of Machine Learning Engineering
• Software Engineering Best Practices for ML
• Continuous Delivery for Machine Learning
• Understanding and Utilizing AutoML Technologies
• MLOps for robust ML application management
• Edge Machine Learning for IoT and distributed systems
• Application of AI APIs in complex solutions

Who is This Course For?

This course is ideal for software developers, data scientists, AI enthusiasts, and IT professionals who want to upgrade their skills in machine learning engineering. It’s also suitable for students and academicians who are venturing into this fast-evolving field.

Real-World Applications

Skills acquired from this course can be applied in various ways:

• Development of AI-powered applications and services.
• Improving existing software products with AI features.
• Creating automated and efficient ML pipelines in the cloud.
• Implementing robust data analysis tools in IoT devices using edge ML.
• Enhancing business processes and customer interactions through AI.

Course Syllabus

Module 1: Introduction to Machine Learning Engineering

• Videos on ML engineering basics, architecture, microservices, and continuous delivery.
• Practical labs, including setting up Flask ML microservices.
• Discussions and quizzes to reinforce learning.

Module 2: Using AutoML

• Deep dive into AutoML concepts with hands-on video guides on tools like Ludwig and Azure ML.
• Interactive sessions on No Code/Low Code solutions.
• Further readings and quizzes to assimulate knowledge.

Module 3: Emerging Topics in Machine Learning

• Videos covering MLOps, edge machine learning, and using AI APIs.
• Practical lab sessions including ML model deployment using Flask.
• Insightful discussions on industry practices and standards.