Arize AI’s Post

What does software architecture for tomorrow look like? Find out on #GOTOpodcast with Sam Newman & Julian Wood. 🎙️ You’ll hear about: • Mastering microservices and asynchronous communication • The importance of clear definitions in tech • The benefits of using Architectural Decision Records (ADRs) • AI's impact and the ongoing need for human expertise 🎧 Tune in now! https://lnkd.in/g_UDEnR

Check out our latest course with DeepLearning.AI, "Embedding Models: from Architecture to Implementation," designed to give you a strong foundation in embedding techniques! Topics you’ll cover include: -Embedding Models: The fundamentals -Advanced Contextualized Token Embedding Methods -Token vs Sentence Embedding Analysis -Training Your Own Dual Encoder -Production-Ready Embedding Models Master the embedding models critical to high-accuracy enterprise RAG deployments and minimize hallucinations. Time to get started! 🚀📚

Missed our webinar on building, securing, and delivering AI applications? No worries—it's available on-demand. Explore the F5 AI Factory Reference Architecture, discover how to optimize AI performance with NGINX in Kubernetes, and dive into the F5 AI Gateway in action via the link in the comments.

AI can now automate the most repetitive and undifferentiated parts of engineering. One of our most popular automation workflows in early access has been rightsizing Kubernetes pods and nodes through the safe, ubiquitous language of engineers: Github PRs. Here’s a look at how Flightcrew operates like Dependabot to proactively reduce costs and protect uptime on Kubernetes. (read more here! https://lnkd.in/gPnNe44Q)

Something that I am learning right now and enjoying the hell out of is Customized Automated AI System Architecture. Still very much a work in progress but is providing me in the end with a blueprint using the Tools and Resources I already have access to or soon will have access to.

In the world of multi-AI interactions, treating each AI model as a unique individual with its own set of strengths is crucial. This blog shows you how to leverage their capabilities effectively and craft prompts that yield the best possible results. Based on my real-world experience in AI solution architecture, this method offers a practical implementation including sample prompts offering valuable insights for professionals seeking to excel in this domain.

The Navie diagram feature visualizes an issue, and Copilot is used to create a tailored solution. Navie AI architect generates code improvements across the code base. Using the Copilot LLM backend helps align with your company compliance and security needs.

I am incredibly excited to share the release of AutoGen v0.4. We have learned alot from all of the emerging simple and complex ways people want to build agents and we believe this version gets us closer to those goals - async stack, event-based messaging, improved observability and control, a low level extremely flexible core api and a high level api with presets, and an improved autogen studio! There is still more work to be done, and we are all excited to make progress with the community! I will be writing more and showing more examples - afterall a framework is only useful to the extent that it can be used to easily express ideas. #aiagents #multiagentsystems #multiagentbook

AutoGen 0.4 is out! 👏 Here is my post reviewing Microsoft AutoGen https://lnkd.in/dJvbjTMY

Meet AutoGen 0.4 from Microsoft! Since releasing AutoGen a year ago, we got to learn how the industry is using AutoGen and where the thinking on AI agents is headed. We used our learnings to rebuild AutoGen for the future. The new version supports flexible collaboration patterns, is scalable and provides stronger observability, control and robustness. Looking forward to the community feedback.