All news with #model monitoring tag

📰 BigQuery now previews TimesFM 3.0, a 330M-parameter foundation model that brings native multivariate time-series forecasting into SQL via AI.FORECAST. TimesFM 3.0 supports forecasting multiple correlated targets, past-only covariates, and known future covariates, and also upgrades univariate forecasting and anomaly detection. The model ranks #1 on major public time-series leaderboards and can be invoked directly in BigQuery SQL or via Conversational Analytics.

🔒 Enterprises are treating AI safety as a static certification instead of a continuous runtime problem. On-paper model certifications like SOC 2 or ISO do not address the unpredictable behaviours that arise when models operate as autonomous agents with API access. The article highlights runtime risks—dynamic tool chaining, state-dependent cascades, and multi-agent feedback loops—and urges continuous monitoring, identity controls, and process-level firewalls to manage agentic threats.

🛠️ Microsoft Foundry presents a unified platform to select, evaluate, optimize, and operate AI models across the full application lifecycle. The post emphasizes that production systems require continuous model selection, validation on real data, cost and latency management, and governance rather than simply picking the most capable model. Foundry adds new model families and Fireworks AI for production-grade open model inference via a single Azure endpoint with enterprise SLAs. It provides model routing, benchmarking with custom datasets, continuous evaluation, and operational controls like versioning, observability, and rollout strategies.

🧾 Amazon SageMaker HyperPod now supports data capture for inference workloads, allowing organizations to record request and response payloads for monitoring, compliance, debugging, and offline analysis. You can capture traffic at the SageMaker endpoint, load balancer, or model pod and combine layers for richer observability. Captured data is delivered asynchronously to Amazon S3 with configurable sampling and encryption using customer-managed AWS KMS keys and is designed to never block inference. Enable data capture via the HyperPod Inference Operator or SageMaker JumpStart.

🔍 Amazon SageMaker HyperPod now provides integrated observability for Restricted Instance Groups (RIG), giving teams training foundation models with Nova Forge a unified view of compute resources and training workloads. A pre-configured Amazon Managed Grafana dashboard, backed by Amazon Managed Service for Prometheus, aggregates metrics from four exporters to show GPU utilization, NVLink bandwidth, CPU pressure, FSx for Lustre usage, network fabric, Kubernetes state, and curated logs including epoch progress, step-level logs, pipeline errors, and Python tracebacks. Observability is automatically enabled for new RIG clusters and can be turned on for existing clusters via the HyperPod console; it is available in all Regions where SageMaker HyperPod RIG is supported.

📊 Amazon Connect now delivers built‑in analytics and monitoring for AI agents across self‑service and agent assist experiences. Administrators can use customizable dashboards to track key metrics such as number of AI‑led interactions, hand‑off rates, conversation turns, and average handle time, and to compare agent versions to find optimal configurations. The release also exposes AI agent traces via APIs and enables rule‑based automation to trigger alerts or actions when conditions like low sentiment transfers occur.