This is a very unique course. First and only course for the AI- Artificial Intelligence for the Network Engineers. It covers AI Applications, use cases for the Networking domains and also cover the fundamental Networking technologies to support AI Infrastructure. 

As I said, first and only course in the world covering both aspects of AI. 

AI for Networking use cases and Network infrastructure for AI workloads and AI tasks, such as Training, Inference for LLMs, Foundational Models, GenAI, etc. 

I’m excited to take you on a journey into the new world of Artificial Intelligence, Machine Learning, and Deep Learning for Network Engineers.

First, we’ll dive into the Introduction to AI in Networking. 

You’ll learn what AI, Machine Learning, and Deep Learning really mean, and more importantly, why they matter to network engineers.

We’ll cover the key drivers that make AI so relevant, like automation, optimization, and analytics and explore how these technologies can enhance network design, operations, and security.

Next, we’ll cover the Foundations of AI, ML, and Deep Learning. 

Here, I will explain the fundamental concepts, terminologies, and applications of AI.

We’ll also look at different types of machine learning such as supervised, unsupervised, and reinforcement learning and also I will discuss how deep learning techniques are becoming increasingly relevant to networking tasks.

Then, we’ll move on to AI and Machine Learning in Network Design. 

This section is full of real-world considerations that network engineers face when integrating AI into their environments.

For instance, we’ll talk about aligning AI implementations with your organization’s business needs, handling data sovereignty issues, and ensuring robust security measures.

You’ll learn how to maintain assurance and integrity in your network, even as you integrate AI-driven processes.

We’ll also cover important factors like the storage and traffic impacts of AI, how to leverage auto scalability, and how to assess the cost-effectiveness and ROI of AI solutions.

Additionally, we’ll discuss governance policies, ethical considerations, and sustainability practices to ensure your AI integrations are both responsible and efficient.

Next I will talk about AI Network Design Use Cases.

In this section, you’ll see practical examples of how AI, ML, and advanced models like large language models can provide predictive insights, recognize patterns, and help identify bottlenecks in your network.

We’ll compare different AI-driven approaches from machine learning and deep learning to generative AI and we will try to understand their impacts on infrastructure, resource requirements, and optimization opportunities.

Our most extensive module in this course, Networking for AI, we’ll focus on the critical role of advanced networking technologies and techniques for AI workloads.

In the High-Performance Networking Technologies part, I will explain the networking technologies designed for low-latency, high-bandwidth communication.

I will explain how RDMA, InfiniBand, RoCE, RoCEv2, iWARP, Ultra Ethernet, and UEC provide the speed and reliability that AI tasks require.

Next, I will go through the Compute Accelerators and Interconnect technologies. 

AI computation relies heavily on specialized hardware. 

I will talk about  DPUs, TPUs, GPUs, FPGA topics.

We’ll also look at interconnect technologies like NVLink and NVMe-over-Fabrics, which help for low latency and high bandwidth between these nodes. 

Also in this module,  I will go through the many Management and Congestion Control mechanisms. 

In this part of the course, I will cover the traffic management strategies at Layer 2 and Layer 3. 

In Layer 2, I will talk about data center bridging technologies such as PFC, ETS, QCN, and DCBX.

In Layer 3, I will explain protocols and methods like ECN, DCQCN, TIMELY, and HPCC; we will see how these technologies can help with end-to-end congestion control in AI network infrastructure.

By understanding these networking concepts and techniques, you’ll be able to optimize the AI networking infrastructure and you will understand how to choose the right technologies and protocols based on given requirements and constraints.

Last but not least, as the technologies and the protocols evolve, I will continue to add new topics to this course, make sure to join our study group after you enroll in this course and participate the discussion in the AI Channel there as well. 

Let’s get started!