Foundations of AI and Machine Learning

Microsoft via Coursera

Coursera based on 98 ratings

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Overview

This course provides a comprehensive introduction to fundamental components of artificial intelligence and machine learning (AI & ML) infrastructure. You will explore the critical elements of AI & ML environments, including data pipelines, model development frameworks, and deployment platforms. The course emphasizes the importance of robust and scalable design in AI & ML infrastructure. By the end of this course, you will be able to: 1. Analyze, describe, and critically discuss the critical components of AI & ML infrastructure and...

Syllabus

Introduction to AI/ML environments

This module provides a comprehensive introduction to the essential elements of AI/ML infrastructure, focusing on the components and processes that underpin effective ML and AI systems. This module will cover the critical aspects of infrastructure required to support robust AI/ML applications, from data handling to model deployment. By the end of this module, you'll have a solid foundation in AI/ML infrastructure, equipping you with the knowledge to contribute to and manage AI/ML projects effectively.

Data management in AI/ML

This module delves into the sophisticated techniques and best practices required for effective data acquisition, cleaning, and preprocessing in the context of AI and ML. Emphasizing the importance of data integrity and security, this module will equip you with the skills needed to manage data sources for various applications, including retrieval-augmented generation (RAG) in large language models (LLMs) and traditional ML systems. You will also learn how to ensure data security throughout the AI development life cycle. By the end of this module, you'll be proficient in advanced data acquisition, cleaning, and preprocessing techniques, and will have a solid understanding of data security best practices, enabling you to manage data effectively and securely in AI development.

Considering and selecting model frameworks

This module offers a comprehensive exploration of popular ML frameworks, libraries, and pretrained LLMs. You will gain hands-on experience with these tools, learning to evaluate their strengths and weaknesses and select the most suitable ones based on specific project needs. By the end of the module, you'll be equipped to implement basic models and adapt their framework choices to optimize performance for diverse applications.

Considerations when deploying platforms

This module provides a detailed exploration of the critical aspects of deploying ML models into production environments. You will learn to identify the key features of deployment platforms, prepare models for real-world use, implement version control for reproducibility, and evaluate platforms based on their scalability and efficiency. By the end of this module, you will be equipped to effectively deploy ML models in production environments, manage their lifecycle with version control, and select the most suitable deployment platforms based on scalability and efficiency considerations.

AI/ML concepts in practice

This module offers an in-depth exploration of the evolving role of AI/ML engineers within corporate environments. You will gain a comprehensive understanding of the responsibilities associated with this role, including data management, framework selection, deployment, version control, and cloud considerations. The module also emphasizes the integration of infrastructure and operations to optimize outcomes and provides strategies for networking and finding mentorship within the AI/ML community. By the end of this module, you will have a clear understanding of the AI/ML engineer's evolving role in the corporate landscape, the key operational priorities for effective infrastructure management, and strategies for building a professional network and finding valuable mentors in the field.