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Sumo Logic Unveils AI Innovations for Dynamic Observability at AWS re:Invent 2024 Sumo Logic unveils its Mo Copilot and Dynamic Observability prototype, integrating AI with log analytics to enhance DevSecOps efficiency at AWS re:Invent 2024.

AI Gateways vs. API Gateways: What’s the Difference? It’s critical to understand their unique roles to properly design AI infrastructure that can handle the requirements of modern applications.

Modern AI Stack: The Emerging Building Blocks for GenAI This clear, high-impact overview of the modern AI stack offers a 10,000-foot perspective on how enterprise architecture is evolving to harness the full potential of AI. Discover the key layers, emerging trends, and game-changing opportunities shaping the future of technology today. Executive Summary  The modern AI stack is undergoing a transformation, shifting from early experimentation to a structured framework that drives enterprise innovation. The new stack is built on four key layers—Compute & Foundation Models, Data, Deployment, and Observability—each enabling enterprises to harness AI's potential efficiently and securely. This evolution has redefined AI development, moving from labor-intensive ML pipelines to product-first approaches powered by advanced large language models (LLMs). As enterprises embrace this new paradigm, they unlock opportunities to innovate faster, reduce costs, and democratize AI capabilities across teams. Key Takeaways 1. AI Maturity Redefined: From Model-First to Product-First  - Traditional AI development required extensive expertise and resources to build custom models. Today, pre-trained LLMs enable teams to start with product-focused innovation, leveraging tools like OpenAI APIs to build applications rapidly. This shift democratizes AI, empowering mainstream developers to drive impactful solutions. 2. Core Infrastructure Layers Powering Innovation  The modern AI stack comprises four essential layers: - Compute & Foundation Models: Training, fine-tuning, and deploying powerful models.  - Data: Tools like vector databases and ETL pipelines provide enterprise-specific context.  - Deployment: Orchestrating AI applications with agent frameworks and prompt management.  - Observability: Monitoring runtime behavior to ensure reliability and security.  These layers collectively form the backbone of enterprise AI systems, ensuring scalability and performance.  3. Key Trends Shaping the Future of AI  - RAG Dominance: Retrieval-augmented generation leads in customizing LLMs with enterprise-specific knowledge, ensuring relevance and accuracy.  - Proliferation of Small, Task-Specific Models: As enterprises seek cost-effective, domain-specific solutions, fine-tuned models are becoming pivotal.  - Serverless Architectures: The shift to serverless computing optimizes costs and simplifies operations, paving the way for seamless scaling and innovation.  This dynamic ecosystem not only accelerates AI adoption but also redefines how enterprises innovate, ensuring AI-driven solutions remain accessible, efficient, and impactful. Link to article --> https://lnkd.in/gGUTvnv2

How to architect AI systems is not a very sexy topic, but is crucial to the success of AI projects. Modularity is a key mechanism for decomposing a system into independent components. Its flipside, composability, is also necessary to avoid microservices-hell. Then, there is the challenge of building batch AI systems, real-time AI systems, and LLM systems. Do you need separate platforms or architectures for all of these? Spoiler - you can do it all with a stateful layer to connect your AI pipelines. https://lnkd.in/dzGjrAY6

It really should go without saying - but *architecting* an AI system matters. If you care about more than just running a bl00dy pipeline, and want to deliver actual value, that is! Take a look at Jim's post. Hopsworks enables open, performant, well-architected systems by implementing well-known software engineering patterns like #modularity and #composability.

Just how good can a truly open source language/code generating AI model be? Turns out, highly promising thanks to platforms like Replicate, which allows anyone to interact with project models like Snowflake's Arctic-Instruct. Constantly impressed with the amount of work people are doing to keep knowledge and tools publicly auditable in a very competitive industry: https://lnkd.in/gFEJ_X3R

🔓 Unlock next-gen #AI and insights with Sumo Logic's new disruptive pricing model! Discover how we are driving a single source of truth for #DevSecOps. Read our CEO Joe Kim's blog for more. #loganalytics

🔓 Unlock next-gen #AI and insights with Sumo Logic's new disruptive pricing model! Discover how we are driving a single source of truth for #DevSecOps. Read our CEO Joe Kim's blog for more. #loganalytics

🔓 Unlock next-gen #AI and insights with Sumo Logic's new disruptive pricing model! Discover how we are driving a single source of truth for #DevSecOps. Read our CEO Joe Kim's blog for more. #loganalytics

The infrastructure and design principles for building with AI continue to evolve. The latest article from Menlo Ventures categorizes these into four layers 1. Compute 2. Data 3. Deployment 4. Observability Surprisingly LLMs companies spend over 90% of their compute cycles and costs on inferencing vs 10% on training/fine-tuning. Also companies demonstrate four phases of working with AI 1. Closed-source model only, aka, working with models from OpenAI or Anthropic with relatively straightforward prompt tweaks or few-shot prompts. 2. RAG, aka transforming their unstructured data sources into vectorized indexes that feed leading closed- and open- source models for working with proprietary data sources 3. Mixture of models. One model is rarely suited for all use cases, and practitioners increasingly deploy a variety of best-fit models for their tasks. The explosion of open- and closed- source model providers gives them choice. 4. Customized models for every task. This is still an emerging area as fine-tuning is still relatively expensive. But as fine tuning approaches become cheaper and more accessible, organizations are starting to broach this more and more.