9/3/2023 0 Comments Dashed filename![]() ![]() To easily monitor your LLMs in production, you can request access to LLM Observability in private beta.īits AI: Datadog’s generative AI interfaceīits AI is your new DevOps copilot, helping you investigate and respond to incidents more efficiently across the Datadog web app, mobile app, and Slack. LLM Observability provides an always-on solution that continuously monitors your LLMs to identify problematic clusters, model drift, and specific prompt and response characteristics that impact model performance. Datadog LLM Observability enables users to observe prompts and responses in order to track model performance, identify opportunities for improvement, and optimize the end-user experience. This may lead to model underperformance or even inaccuracies such as model hallucinations that create business and reputational risks. While implementation of LLM-powered applications has become easier, it is difficult to gain visibility into the underlying LLMs as application developers and machine learning engineers have limited control of or insight into how these pretrained models operate. The introduction of pre-trained large language models (LLMs) such as GPT and BERT has revolutionized the usage of generative AI technology. With our 12 new AI integrations-including our NVIDIA DCGM Exporter integration-you can access out-of-the-box dashboards with visualizations and metrics tailored to each component of your stack, enabling you to ensure that your models effectively scale according to your business needs. Integrating machine-learning models into your applications and workflows often means leveraging a number of specialized technologies, including vector databases like Pinecone and Weaviate, development platforms like Vertex AI and SageMaker, and discrete GPUs from providers like CoreWeave. LLM-powered observability and your AI ecosystem Integration roundup: Monitoring your AI stackĪs AI development accelerates across industries, Datadog is at the forefront of helping you gain visibility into every layer of your AI-optimized tech stack, from infrastructure and data storage to models and service chains. In this post, we recap these new offerings-as well as all the other key announcements from DASH 2023-and help you get started using them to gain deeper visibility into your environment. We also expanded our developer-focused features by adding static code analysis and end-to-end mobile monitoring. We introduced new products that help you secure your cloud infrastructure, find vulnerabilities in your application code, and conduct historical security investigations. And Bits AI, our new DevOps copilot, helps speed up the detection and resolution of issues across your environment. With Datadog’s new AI integrations, you can easily monitor every layer of your AI stack. This year at DASH, we announced new products and features that enable your teams to get complete visibility into their AI ecosystem, utilize LLM for efficient troubleshooting, take full control of petabytes of observability data, optimize cloud costs, and more. Alert on Database Monitoring query samples and explain plans.Automatically correlate database query metrics and request traces.Network Topology Map for port-to-port device connectivity.Jumpstart network investigations with an updated story-centric UX for NPM.Serverless Monitoring for AWS Step Functions.Visualize container metrics from OpenTelemetry Collector.Understand and optimize your cloud resources and costs.Historical security investigations with Cloud SIEM Investigator.Application Security Management - API Security.Mitigate vulnerabilities with Datadog Infrastructure Vulnerability Management.Protect against IAM-based attacks with Datadog CIEM. ![]() Define teams using identity providers data and control their access to individual resources within Datadog.Enhanced visibility into service-to-service connections and inferred services.Reproduce exceptions with production variable snapshots in APM.Understand the business impact of backend errors with Trace Queries. ![]()
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