Enroll Course: https://www.coursera.org/learn/ai-infrastructure-operations-fundamentals
Artificial Intelligence (AI) is no longer a futuristic concept; it’s a present-day reality reshaping industries and unlocking unprecedented possibilities. From the voice assistant on your phone to the complex algorithms powering self-driving cars and the revolutionary capabilities of generative AI, AI is here to stay. For enterprise professionals looking to harness the power of AI, understanding its underlying infrastructure and operational needs is paramount. This is precisely where Coursera’s ‘AI Infrastructure and Operations Fundamentals’ course, developed with NVIDIA Training, shines.
This course is thoughtfully designed for professionals aiming to navigate the dynamic landscape of AI. Whether you’re a seasoned tech expert or just embarking on your AI journey, this course offers invaluable insights. It demystifies the core components that make AI possible, starting with a solid introduction to AI, Machine Learning (ML), and Deep Learning (DL). A significant portion of the initial module is dedicated to Generative AI and Large Language Models (LLMs), explaining their workings and the new business avenues they are opening up. Crucially, it breaks down the role of Graphics Processing Units (GPUs) versus Central Processing Units (CPUs) and explores the software ecosystem that empowers developers to leverage GPU computing for data science. The module concludes by addressing the critical considerations for deploying AI workloads across various infrastructures, from on-premises data centers to multi-cloud environments.
The second module dives deep into ‘AI Infrastructure,’ focusing on the practicalities of building and managing AI clusters. You’ll gain a comprehensive understanding of the requirements for multi-system AI clusters, including the specific capabilities of NVIDIA GPUs and CPUs tailored for AI workloads. Storage and networking considerations are also thoroughly covered. Furthermore, the course highlights the importance of energy-efficient computing practices in reducing the carbon footprint of data centers and introduces the concept of Reference Architectures (RAs) as a foundation for building optimized AI systems. The module rounds off with an exploration of how cloud computing enhances AI deployments and the key factors to consider when implementing AI in the cloud.
Finally, the ‘AI Operations’ module equips you with the knowledge of infrastructure management, monitoring, cluster orchestration, and job scheduling. You’ll learn about provisioning, managing, and monitoring AI infrastructure, and the value of cluster management tools. The distinctions and common tools used for orchestration and scheduling are clearly explained, along with the significant benefits of MLOps tools for achieving continuous delivery and automation of AI workloads.
Overall, ‘AI Infrastructure and Operations Fundamentals’ is an exceptional course for anyone serious about understanding the practicalities of AI deployment and management. It provides a clear, structured, and comprehensive overview of essential concepts, making complex topics accessible. The insights from NVIDIA, a leader in AI hardware and software, add significant weight and credibility to the content. I highly recommend this course to IT professionals, data scientists, and business leaders who want to build a strong foundation in AI infrastructure and operations.
Enroll Course: https://www.coursera.org/learn/ai-infrastructure-operations-fundamentals