AI Infrastructure Learning Resources
With the increasing demand for scalability and efficiency in AI workloads, organizations require optimized data center solutions that accelerate data processing and enhance security for handling large-scale AI data sets. Whether you're a seasoned professional or just starting out, our selection of learning resources will provide you with valuable insights and practical knowledge on how to harness the power of GPUs to scale your AI applications.
AI Application Architecture
Discover the best practices and design principles for building and deploying robust artificial intelligence applications with accelerated AI performance and scalability.
AI Inference
Leverage deep learning training and inferencing across clouds and in on-prem data centers. Delve into resources on deploying AI models for real-time decision-making and efficient performance.
Edge AI
Edge AI represents a transformative shift in the deployment of artificial intelligence, allowing algorithms to process data closer to the source with reduced latency, rather than relying on centralized data centers. By leveraging GPUs or edge-optimized models, AI tasks can be accelerated, making Edge AI ideal for real-time automation across various ecosystems, including smart cities, healthcare, industrial IoT, and telecommunications. However, the deployment of AI workloads at the edge also presents challenges such as ensuring data security, managing power and cooling efficiently, and maintaining scalability. Explore the multifaceted benefits and challenges of deploying AI workloads at the edge.
Scaling AI and ML Workloads
Find strategies and tools to efficiently scale and automate your generative AI and machine learning workloads to handle increasing data and computational demands. Learn how to optimize training data throughput.
AI Factories
As organizations incorporate AI models into all aspects of their application ecosystem, AI factories provide the necessary infrastructure of storage, networking and compute for high-performance training and inference. Understand the concept of AI factories and how they streamline the production and deployment of AI models in an enterprise.
AI Data Management
Delve into the methodologies and technologies to effectively manage, store, and prepare data for AI model development and consolidate it for contextually aware AI model inference.
Retrieval-Augmented Generation (RAG)
Explore how data mobility is critical for combining foundational AI models with proprietary data to deliver more accurate and contextually aware AI outputs.
AI Industry Trends
The landscape of the AI infrastructure is rapidly evolving, driven by advancements in artificial intelligence and how it's being integrated into every app and ecosystem. Following are a few perspectives from F5 on the latest AI trends and innovations impacting enterprises.
F5 AI Collaboration and Partnerships
Discover how F5 collaborates with partners to drive advancements and deliver cutting-edge AI solutions.
