AI-related announcements dominate once more. New models for Bedrock. Vector search everywhere, and DynamoDB does a bait-and-switch. Welcome to day 3 of re:Invent 2023.

The advent of Machine Learning (ML) has unlocked new possibilities in various domains, including full lifecycle Application Performance Monitoring (APM). Maintaining peak performance and seamless user experiences poses significant challenges with the diversity of modern applications. So where and how does ML and APM fit together? Traditional monitoring methods are often reactive, resolving concerns after the process already affected the application’s performance.

Elastic’s innovative investments to support an open ecosystem and a simpler developer experience In this blog, we want to share the investments that Elastic® is making to simplify your experience as you build AI applications. We know that developers have to stay nimble in today’s fast-evolving AI environment. Yet, common challenges make building generative AI applications needlessly rigid and complicated. To name just a few.

We are pleased to share a sneak peek of Query Assistant, our latest innovation that bridges the world of declarative querying with Generative AI. Leveraging our large language models (LLMs), Coralogix’s Query Assistant translates your natural language request for insights into data queries. This delivers deep visibility into all your data for everyone in your organization.

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.

Generative AI has the world thinking about automation now more than ever before. The Information Technology Infrastructure Library (ITIL) has prioritized it from the start. ITIL has advocated for automation as a transformative tool for organizations to deliver business value, accelerate change, and reinvent service configuration management. By handling mundane tasks, automation can empower people to do more innovative and effective work.

Generative AI is revolutionizing the way businesses operate, from improving operational resilience to mitigating security risks and enhancing customer experiences. In a recent roundup of c-suite insights from three IT leaders — Matt Minetola, CIO, Mandy Andress, CISO, and Rick Laner, chief customer officer — we gain a comprehensive understanding of how generative AI is being used to improve business outcomes across organizations.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. In this edition, we are exploring the emerging market for climate technology, exploring their significance, and addressing why a successful path forward lies in embracing clean, green, and planet-friendly solutions for both startups and established companies. Let’s dive right in.

In incident management, staying ahead of the curve is crucial, and that's what we're doing with our latest suite of features designed to streamline your workflow and enhance your response capabilities. Furthermore, you have provided numerous excellent suggestions during this period. We value your feedback and invite you to reach out to us at support@ilert.com to share your experiences with ilert.

Approximately 65,000 new implementers will be needed by 2027 as a result of AI, according to research by ServiceNow and Pearson. The time is now to enter this dynamic and high-growth field. Let’s explore what an implementer is, how the role is changing, and how you can prepare to fill an open position.

ChatGPT captured our collective imagination when it burst into the mainstream last year, setting off a hype cycle that hasn’t abated. The enterprise is where generative AI (GenAI) will become more than tech’s newest shiny object. GenAI is transforming the way we work, unlocking new efficiencies, driving productivity, and creating employee and customer experiences we never could have imagined. ServiceNow is at the forefront of this transformation.

This week, NVIDIA unveiled what they are calling “the world’s most powerful GPU for supercharging AI and HPC workloads,” the H200 Tensor Core GPU. There is much hype around the H200 as it is the first GPU with HBM3e. The larger and faster memory will further enable generative AI, large language models, and advance scientific computing for HPC workloads. Read the NVIDIA press release.

At Catchpoint, our philosophy is that AI should not be adopted simply for the sake of AI itself. Instead, it should be embraced when it proves to be the most effective solution for addressing a particular business challenge. While the world is currently in the fervor of the oncoming AI revolution, our industry-leading IPM platform has quietly harnessed the potential of Artificial Intelligence for years.

Generative AI is forecasted to have a massive impact on the economy. These headlines are driving software teams to rapidly consider how they can incorporate generative AI into their software, or risk falling behind in a sea-change of disruption. But in the froth of a disruptive technology, there’s also high risk of wasted investment and lost customer trust.

With this guide, empower your SRE team to achieve enhanced alert remediation and incident management.

For the past few months, we’ve been working closely with the LangChain team as they made progress on launching LangServe and LangChain Templates! LangChain Templates is a set of reference architectures to build production-ready generative AI applications. You can read more about the launch here.

Generative AI has already shown its huge potential, but there are many applications that out-of-the-box large language model (LLM) solutions aren’t suitable for. These include enterprise-level applications like summarizing your own internal notes and answering questions about internal data and documents, as well as applications like running queries on your own data to equip the AI with known facts (reducing “hallucinations” and improving outcomes).

Organizations saw a 243% ROI and $1.2 million in savings over three years In today’s complex and distributed IT environments, traditional monitoring falls short. Legacy tools often provide limited visibility across an organization’s tech stack and often at a high cost, resulting in selective monitoring. Many companies are therefore realizing the need for true, affordable end-to-end observability, which eliminates blind spots and improves visibility across their ecosystem.

Top tips is a weekly column where we highlight what’s trending in the tech world and list ways to explore these trends. When you think about generative AI, what instinctively comes to your mind is content and image generation. But, in this week’s Top tips column, let’s look at a less-explored facet of generative AI: data analytics. There are a lot of conversations about data and its benefits.

In the ever-changing field of artificial intelligence, OpenAI is consistently seen as a leader in innovation. Its AI models, starting with GPT-3 and now with GPT-4, are already used extensively in software development and content creation, and they’re expected to usher in entire sets of new systems in the future.

Generative artificial intelligence (AI) is a form of AI that can create new, original content such as text, code, images, video, and even music. Generative AI-powered tools like GitHub’s Copilot and OpenAI’s ChatGPT have the potential to revolutionize the way you develop software, enabling you to be more efficient and creative. Used in the right way, generative AI can streamline workflows, accelerate development cycles, and unlock the potential for innovation.