Description
The Complete Guide to AI Infrastructure: Zero to Hero. This course is a complete training program designed to master all aspects of AI infrastructure. It starts with Linux basics, cloud computing on platforms like AWS and Azure, and working with GPUs, and then moves on to more advanced topics including containerization with Docker, orchestration with Kubernetes, and automation with Helm. It then covers essential topics like managing data through object storage and data lakes, building data pipelines with Kafka, and CUDA programming to optimize GPU performance and distributed training with tools like PyTorch and TensorFlow. The main part of the course focuses on MLOps, where you’ll learn how to implement test tracking with MLflow, build CI/CD pipelines with GitHub Actions and Jenkins, and deploy models with inference servers like TorchServe and NVIDIA Triton. It also covers skills in monitoring and scaling services in production with tools like Prometheus and Grafana. Advanced sections include topics such as security and GDPR compliance, cost optimization using instant instances and autoscaling, as well as deploying at the edge with hardware like NVIDIA Jetson and working with large language models and techniques like RAG. Combining theoretical foundations with practical projects, this course equips learners to design and manage an enterprise, production-ready AI infrastructure.
What you will learn
- Mastering the basics of AI infrastructure:
- Understand the basics of AI infrastructure including Linux, cloud computing, CPU vs. GPU, and why infrastructure is important to powering modern AI systems.
- Deployment and management in the cloud:
- Deploy and manage GPU-powered cloud instances on AWS, Google Cloud, and Azure, comparing cost, performance, and scaling options for AI workloads.
- Containerization and organization:
- Build, package, and deploy AI applications using Docker containers, Kubernetes orchestration, and Helm charts for efficient multi-service infrastructure.
- GPU distributed optimization and training:
- Optimize GPU performance with CUDA, NVLink, and memory hierarchy, while mastering distributed AI training with PyTorch, TensorFlow, and Horovod.
- Implementing MLOps pipelines:
- Implement MLOps pipelines with MLflow, CI/CD tools, and model registries, ensuring reproducibility, versioning, and continuous delivery of AI models.
- Model presentation and scaling:
- Render and scale models using FastAPI, TorchServe, and NVIDIA Triton, with load balancing and monitoring for high-performance AI inference systems.
- Monitoring, Security and Optimization:
- Monitor, secure, and optimize your AI infrastructure with Prometheus, Grafana, IAM, anomaly detection, encryption, and cost-saving cloud strategies.
- Practical and final project:
- Complete over 50 hands-on experiments and a final project to confidently design, deploy, and deliver a full-scale, production-ready AI infrastructure system.
This course is suitable for people who:
- Aspiring AI engineers who want to build production-ready AI systems step by step from scratch.
- Data scientists and ML professionals who are ready to move beyond modeling and into deploying, delivering, and managing AI workloads.
- Software engineers and DevOps professionals looking to add AI infrastructure, MLOps, and Kubernetes skills to their toolbox.
- Cloud engineers and system administrators interested in optimizing GPU clusters, storage, and cost for AI workloads.
- Students, researchers, or beginners curious about Linux, the cloud, GPUs, and AI pipelines, no prior experience required.
- Startup founders and technology leaders who want to understand how to build scalable, secure, and cost-effective AI infrastructure for their organizations.
Course details
- Publisher: Udemy
- Instructor: School of AI
- Training level: Beginner to advanced
- Training duration: 60 hours and 57 minutes
- Number of lessons: 366
Course topics
Prerequisites for The Complete Guide to AI Infrastructure: Zero to Hero
- No prior experience required – this course takes you from beginner to advanced, step by step.
- A basic understanding of programming (Python recommended) will help but is not mandatory.
- Familiarity with cloud platforms (AWS, GCP, or Azure) is helpful, but we cover the fundamentals.
- Access to a computer with internet and the ability to install free tools like Docker and Python.
- Optional: GPU access (local or cloud) for running deep learning workloads – we guide you through setup.
- Curiosity, willingness to learn, and commitment to completing hands-on labs each week.
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: English
Quality: 720p
Download link
Downloadly
Rapidgator link
File(s) password: www.downloadly.ir
File size
19.1 GB
