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.
Foundations of AI and Machine Learning
This course is part of Microsoft AI & ML Engineering Professional Certificate
Instructor: Microsoft
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Recommended experience
Skills you'll gain
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November 2024
31 assignments
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There are 5 modules in this course
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.
What's included
14 videos18 readings9 assignments
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.
What's included
7 videos19 readings7 assignments
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.
What's included
7 videos18 readings5 assignments
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.
What's included
7 videos16 readings6 assignments
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.
What's included
7 videos16 readings4 assignments1 peer review
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Frequently asked questions
Awareness of common business processes and workflows in a corporate context, specifically, but not limited to:
Monitoring
Reporting
Ticketing
Troubleshooting/Debugging
Quality Testing
Escalation
Awareness of common corporate approaches to technology infrastructure and operations, specifically, but not limited to:
Governance
Policy and Protocol
Version Control
Cloud Architecture
Continuous Integration/Delivery (CI/CD)
DevSecOps Practices
Agile Practices and Tools
Basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pre-trained large language models (LLM).
Intermediate programming knowledge of Python.
Familiarity with statistics is also recommended.
You will need a license to Microsoft Azure (or a free trial version) and appropriate hardware. Note: the free trial version of Azure is time limited and may expire before completion of the program.
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.