Operations | Monitoring | ITSM | DevOps | Cloud

We had the opening keynote by Adam Selipsky. If you missed the live stream, you can watch it on YouTube here. Unsurprisingly, so much of the keynote was about AI.

For modern enterprises aiming to innovate faster, gain efficiency, and mitigate the risk of failure, operational resilience has become a key competitive differentiator. But growing complexity, noisy systems, and siloed infrastructure have created fragility in today’s IT operations, making the task of building resilient operations increasingly challenging.

Does this sound familiar? The incident has just been resolved and management is putting on a lot of pressure. They want to understand what happened and why. Now. They want to make sure customers and internal stakeholders get updated about what happened and how it was resolved. ASAP. But putting together all the needed information about the why, how, when, and who, can take weeks. Still, people are calling and writing. Nonstop.

Ever since we launched Query Assistant last June, we’ve learned a lot about working with—and improving—Large Language Models (LLMs) in production with Honeycomb. Today, we’re sharing those techniques so that you can use them to achieve better outputs from your own LLM applications. The techniques in this blog are a new Honeycomb use case. You can use them today. For free. With Honeycomb.

This tutorial guides you on how to use the Amazon SageMaker Orb to orchestrate model deployment to endpoints across different environments. It also shows how to use the CircleCI platform to monitor and manage promotions and rollbacks. It will use an example project repository to walk you through every step, from training a new model package version to deploying your model across multiple environments.

Top tips is a weekly column where we highlight what’s trending in the tech world today and list out ways to explore these trends. This week, we’re examining four use cases for AI in the ever-growing FinTech sector. The FinTech sector has transformed the discussion around the financial services industry from top to bottom.

Amazon Bedrock is a fully managed service that offers foundation models (FMs) built by leading AI companies, such as AI21 labs, Meta, and Amazon along with other tools for building generative AI applications. After enabling access to validation and training data stored in Amazon S3, customers can fine-tune their FMs to invoke tasks such as text generation, content creation, and chatbot Q&A—without provisioning or managing any infrastructure.