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Datadog

Datadog on LLMs: From Chatbots to Autonomous Agents

Jun 27, 2024 By Datadog In Datadog
As companies rapidly adopt Large Language Models (LLMs), understanding their unique challenges becomes crucial. Join us for a special episode of "Datadog On LLMs: From Chatbots to Autonomous Agents," streaming directly from DASH 2024 on Wednesday, June 26th, to discuss this important topic. In this live session, host Jason Hand will be joined by Othmane Abou-Amal from Datadog’s Data Science team and Conor Branagan from the Bits AI team. Together, they will explore the fascinating world of LLMs and their applications at Datadog.
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DASH 2024: Guide to Datadog's newest announcements

Jun 26, 2024 By Datadog In Datadog
At this year’s DASH, we announced new products and features that enable your team to observe your environment, secure your infrastructure and workloads, and act to remediate problems before they affect customers. LLM Observability, which enables you to get deep visibility into your generative AI applications, is now generally available. The Datadog Agent now includes an embedded OTel Collector to provide native support for OpenTelemetry.
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Unify your OpenTelemetry and Datadog experience with the embedded OTel Collector in the Agent

Jun 26, 2024 By Lutao Xie In Datadog
OpenTelemetry (OTel) is an open source, vendor-neutral observability solution that consists of a suite of components—including APIs, SDKs, and the OTel Collector—that allow teams to monitor their applications and services in a standardized format. OTel defines this data via the OpenTelemetry Protocol (OTLP), a standard for the encoding and transfer of telemetry data that organizations can use to collect, process, and export telemetry and route it to observability backends, such as Datadog.
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Monitor, troubleshoot, improve, and secure your LLM applications with Datadog LLM Observability

Jun 26, 2024 By Thomas Sobolik In Datadog
Organizations across all industries are racing to adopt LLMs and integrate generative AI into their offerings. LLMs have been demonstrably useful for intelligent assistants, AIOps, and natural language query interfaces, among many other use cases. However, running them in production and at an enterprise scale presents many challenges.
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SLOs 101: How to establish and define service level objectives

Jun 24, 2024 By Mark Azer In Datadog
In recent years, organizations have increasingly adopted service level objectives, or SLOs, as a fundamental part of their site reliability engineering (SRE) practice. Best practices around SLOs have been pioneered by Google—the Google SRE book and a webinar that we jointly hosted with Google both provide great introductions to this concept. In essence, SLOs are rooted in the idea that service reliability and user happiness go hand in hand.
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Track the status of all your SLOs in Datadog

Jun 24, 2024 By Meghan Jordan In Datadog
Service level objectives, or SLOs, are a key part of the site reliability engineering toolkit. SLOs provide a framework for defining clear targets around application performance, which ultimately help teams provide a consistent customer experience, balance feature development with platform stability, and improve communication with internal and external users.
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Best practices for managing your SLOs with Datadog

Jun 24, 2024 By Mark Azer In Datadog
Collaboration and communication are critical to the successful implementation of service level objectives. Development and operational teams need to evaluate the impact of their work against established service reliability targets in order to improve their end user experience. Datadog simplifies cross-team collaboration by enabling everyone in your organization to track, manage, and monitor the status of all of their SLOs and error budgets in one place.
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Troubleshoot infrastructure faster with Recent Changes

Jun 21, 2024 By Sriram Raman In Datadog
Infrastructure changes often trigger incidents, but troubleshooting these incidents is challenging when responders have to navigate through multiple tools to correlate telemetry with configuration changes. This lack of unified observability leads to longer mean time to resolution (MTTR), greater operational stress, and ultimately, negative business outcomes.
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Diagnose runtime and code inefficiencies in production by using Continuous Profiler's timeline view

Jun 20, 2024 By Guillaume Turbat In Datadog
When you face issues like reduced throughput or latency spikes in your production applications, determining the cause isn’t always straightforward. These kinds of performance problems might not arise for simple reasons such as under-provisioned resources; often, the root of the problem lies deep within an application’s runtime execution.
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