# AI, ML & Data Engineering Software engineering disciplines focused on artificial intelligence, machine learning, and data engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Mastering Edge AI on Raspberry Pi with LiteRT and Gemma DevFeed: [Mastering Edge AI on Raspberry Pi with LiteRT and Gemma]() Original publisher: [Read original article]() Author: Lu Wang; Terry Heo; Naushir Patuck; José María Casanova Published: 2026-09-12T11:04:33.891311Z Content type: tutorial Language: en Sources: [Google Developers Blog]() Topics: [LiteRT](), [On-device AI](), [Local AI](), [AI, ML & Data Engineering](), [Google AI](), [cpu](), [Security, Privacy and Abuse Prevention]() Tags: [cli](), [cpu](), [edge](), [gemma](), [gpu](), [inference](), [litert](), [offline](), [on-device](), [real-time](), [robotics]() ### AI overview The article explains how to deploy Gemma models with LiteRT on a Raspberry Pi for local, real-time edge AI applications such as robotics. It highlights LiteRT-LM, CPU and GPU optimization, and reported performance figures for Gemma 4 E2B on Raspberry Pi 5. ### Source excerpt Deploying secure, real-time Edge AI on Raspberry Pi is now simplified using LiteRT and lightweight Gemma open models. LiteRT optimizes CPU and GPU performance, delivering fast token speeds for models like Gemma4, enabling real-time local reasoning for robotics. Developers can quickly convert, quantize, and run these models using the lightweight LiteRT CLI tool. Support for Hailo AI accelerators is also coming very soon. ## AI more likely to kill animals if it saves fuel or money DevFeed: [AI more likely to kill animals if it saves fuel or money]() Original publisher: [Read original article]() Author: Thomas Claburn Published: 2026-09-11T21:49:59Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [AI, ML & Data Engineering]() Tags: [ai](), [ai-and-ml](), [ai-safety](), [artificial-intelligence](), [harvestbench](), [llms](), [machine-learning](), [models](), [openai]() ### AI overview The article reports that AI is more likely to kill animals when doing so saves fuel or money. ### Source excerpt Machine learning models still have a lot to learn about the value of life ## Oracle says AI will save it from the SaaSpocalypse, not bring it on DevFeed: [Oracle says AI will save it from the SaaSpocalypse, not bring it on]() Original publisher: [Read original article]() Author: Simon Sharwood Published: 2026-09-11T02:43:10Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [AI, ML & Data Engineering]() Tags: [ai](), [ai-and-ml](), [applications](), [oracle](), [saas](), [sales](), [software](), [speed]() ### AI overview Oracle says AI can provide a better interface, speed installations, and drive IaaS sales rather than cause a SaaS decline. ### Source excerpt It's a better interface, can speed installations, and drives IaaS sales too ## DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation DevFeed: [DiscoSign: Discourse-Aware Text to Sign Language Gloss Translation]() Original publisher: [Read original article]() Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [AI, ML & Data Engineering](), [Language models]() Tags: [evaluation](), [experiments](), [framework](), [llm](), [metrics]() ### AI overview DiscoSign is an LLM-based framework for translating text to sign-language gloss while preserving discourse-level coherence. It targets spatial coreference, Question-Answer Clauses, and consistent English-concept-to-ASL-sign mappings, with evaluation metrics for these dimensions. ### Source excerpt Sign language processing systems have traditionally operated at the sentence level, ignoring critical discourse phenomena fundamental to sign language comprehension. We introduce DiscoSign, a computational approach for discourse-aware text to sign language gloss translation grounded in linguistic research. We address three key phenomena within our modular Large Language Model (LLM)-based translation framework: (i) spatial coreference resolution, where entities maintain consistent spatial locations throughout discourse; (ii) Question-Answer Clauses (QACs), pseudocleft structures serving... ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size. DevFeed: [Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size.]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-10T09:00:43Z Content type: news Language: en Sources: [The New Stack]() Topics: [AI, ML & Data Engineering]() Tags: [ai](), [ai-infrastructure](), [ai-models](), [ai-operations](), [cuopt](), [deployment](), [nemotron](), [nvidia](), [partnership](), [supply-chain](), [training]() ### AI overview Nvidia and Palantir are fine-tuning Nemotron for Nvidia supply-chain decisions as a sovereign-AI deployment. The companies say the 30B-parameter model outperforms a model 18 times larger and plan to extend lessons from the deployment to other sectors. ### Source excerpt Nvidia and Palantir announced Thursday that they're working together to bring "sovereign AI to critical supply chains," kicking off initially The post Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia's supply chain. It beats a model 18 times its size. appeared first on The New Stack. ## Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock DevFeed: [Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock]() Original publisher: [Read original article]() Author: Tanvi Girinath Published: 2026-09-08T22:06:58Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [AI, ML & Data Engineering](), [AI-assisted coding](), [AI Chat]() Tags: [agents](), [amazon-bedrock](), [announcements](), [api](), [aws](), [cache](), [chatgpt](), [codex](), [gpt](), [intermediate-200](), [openai](), [scalability](), [security]() ### AI overview GPT-6 Astra is generally available on Amazon Bedrock. The article describes its reasoning, codebase work, browser use, prompt caching, and enterprise controls for production workloads. ### Source excerpt GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your most demanding tasks, running on the Amazon Bedrock inference engine built for high performance, security, and scale. ## Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes DevFeed: [Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes]() Original publisher: [Read original article]() Author: Mona Mona Published: 2026-09-08T18:29:15Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [AI, ML & Data Engineering](), [Training AI Models](), [AWS IAM]() Tags: [amazon-dynamodb](), [amazon-elasticache](), [amazon-machine-learning](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [announcements](), [api](), [artificial-intelligence](), [aws-identity-and-access-management-iam](), [intermediate-200](), [technical-how-to]() ### AI overview Amazon SageMaker Feature Store adds the UpdateRecord API for atomic feature-level updates without reading or rewriting an entire record. ### Source excerpt Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more feature values in a single call without reading or rewriting the entire record. It is available for both the Standard (Amazon DynamoDB) and In-Memory (Amazon ElastiCache) online store tiers. ## Measuring real-time performance per dollar under continuous load: CostBench's first end-to-end results DevFeed: [Measuring real-time performance per dollar under continuous load: CostBench's first end-to-end results]() Original publisher: [Read original article]() Author: Tom Schreiber; Lionel Palacin Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [AI, ML & Data Engineering](), [Amazon Redshift]() Tags: [analytics](), [architecture](), [benchmark](), [bigquery](), [clickhouse](), [cloud](), [cost](), [data](), [performance](), [real-time](), [serverless](), [streaming](), [systems](), [tracing](), [work]() ### AI overview CostBench benchmarks the cost and performance of real-time cloud data warehouses under continuous ingestion and query load. It compares ClickHouse Cloud with Snowflake, BigQuery, and Redshift Serverless, reporting better end-to-end performance per dollar for ClickHouse Cloud in the tested workload. ### Source excerpt CostBench puts cloud data warehouses under continuous load. Across the complete path from fresh data to fast answers, ClickHouse Cloud delivers 412-1,996x better performance per dollar. ## Data Engineering Weekly #286 DevFeed: [Data Engineering Weekly #286]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-09-07T00:18:06Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [agent observability](), [Large Language Model](), [data-modeling](), [Monitoring](), [Scalability](), [semantic-layer](), [parquet]() Tags: [ai](), [ai-agents](), [data-engineering](), [data-modeling](), [llm](), [monitoring](), [newsletter](), [observability](), [parquet](), [scalability](), [semantic-layer](), [weekly]() ### AI overview Data Engineering Weekly #286 is a curated newsletter covering data platform fundamentals, mathematics for machine learning, agentic machine learning at Instacart, Netflix's lifecycle for LLM-as-a-Judge systems, semantic layers and data modeling for AI analytics, and Apache Pinot scalability. ### Source excerpt The Weekly Data Engineering Newsletter ## How to Carry User Identity Across Federated Kubernetes and AI Platforms DevFeed: [How to Carry User Identity Across Federated Kubernetes and AI Platforms]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-03T22:36:02Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Platform](), [Kubernetes](), [AI, ML & Data Engineering]() Tags: [ai-platform](), [ai-platforms-deployment](), [api](), [aws](), [cloud](), [cloud-services](), [data](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [identity](), [infrastructure](), [kubernetes](), [software-defined-data-center]() ### AI overview The article presents a central identity-gateway pattern for carrying user identity across federated Kubernetes, data, and AI platforms. It uses OIDC, a shared session store, stateless data-plane gateways, and an identity-validation API to establish trusted local identity context without distributing raw tokens to every application. ### Source excerpt Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook... ## Claude Fable 5.1 now available on AI Gateway DevFeed: [Claude Fable 5.1 now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-09-01T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Language models](), [AI-assisted coding](), [AI, ML & Data Engineering](), [Vulnerabilities](), [cursor]() Tags: [agentic](), [agentic-coding](), [ai-gateway](), [anthropic](), [api](), [claude](), [language-models](), [safety](), [vulnerabilities]() ### AI overview Claude Fable 5.1 is available on AI Gateway, with model fallback support for safety-classifier refusals and compatibility across its API formats. ### Source excerpt Claude Fable 5.1 from Anthropic is now available on AI Gateway. Fable 5.1 improvements compared to previous Claude models are concentrated in long, multi-stage work like agentic coding, knowledge work, and research that takes several rounds of searching and following up. Anthropic ships Fable 5.1 with cybersecurity and biology safety classifiers enabled. Finding vulnerabilities in source code is allowed, but some routine coding and debugging may still be refused. To ensure requests are still serviced when the safety classifiers are triggered, use model fallbacks. Add a models array to providerOptions.gateway listing the models to try. AI Gateway sends the request to Fable 5.1 first, and if Anthropic refuses it, works down the array in order and returns the response from the first model that succeeds: This request falls back to Opus 5, then Sonnet 5, if a safety classifier is triggered. The same models option works on every AI Gateway API format, including Chat Completions, Messages, and OpenAI Responses. Try Fable 5.1 in the model playground. To use it in a coding agent, see the coding agents guide, then run vercel ai-gateway coding-agents setup to connect agents like Claude Code, Codex, OpenCode, Cursor, Pi, and more and select anthropic/claude-fable-5.1 inside the agent. Anthropic does not support Zero Data Retention for Fable 5.1. Prompts and completions are retained for 30 days and are not used to train Claude. You can view all language models available on AI Gateway. Read more ## Data Engineering Weekly #285 DevFeed: [Data Engineering Weekly #285]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-31T02:51:19Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Data Quality](), [observability](), [Artificial Intelligence]() Tags: [agentic-ai](), [automated](), [data-engineering](), [newsletter](), [observability](), [privacy](), [python](), [quality](), [recovery](), [weekly]() ### AI overview Data Engineering Weekly #285 covers building data platforms, AI chip architectures, preparing data for agentic AI, post-AI data stacks, data modernization, automated data contract breach handling, and privacy-preserving measurement tools. ### Source excerpt The Weekly Data Engineering Newsletter ## Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect DevFeed: [Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-08-28T17:06:28Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering](), [C++](), [Orchestration]() Tags: [ai](), [ai-inference](), [ai-models](), [api](), [applications](), [c-plus-plus](), [data-center-cloud](), [deployment](), [developer-tools-techniques](), [edge-computing](), [hugging-face](), [inference](), [integration](), [kernel](), [model](), [nvidia](), [open](), [open-source](), [orchestration](), [python](), [pytorch](), [tensorrt]() ### AI overview The article explains NVIDIA TensorRT Model Connect, a collection of modifiable reference implementations for deploying supported open models from a Hugging Face ID or local checkpoint to native C++ inference. It describes a two-phase deployment bundle workflow, semantic and module-level C++ APIs, and custom GPU-kernel integration. ### Source excerpt Open AI models are evolving faster than ever, but bringing them into native applications can still require model-specific conversion, preprocessing,... ## Hy4 Preview now available on AI Gateway DevFeed: [Hy4 Preview now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-08-28T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Language models](), [vercel ai sdk](), [AI-assisted coding](), [AI, ML & Data Engineering]() Tags: [ai-gateway](), [api](), [coding](), [inference](), [language-models](), [performance]() ### AI overview Hy4 Preview, Tencent's open-source Mixture-of-Experts model, is now available through AI Gateway. The release describes AI SDK and coding-agent setup, along with gateway features for model access, usage tracking, routing, and pricing. ### Source excerpt Hy4 Preview from Tencent is now available on AI Gateway. Hy4 Preview is an open-source Mixture-of-Experts model with 770B total parameters and 49B active per token, aimed at long-horizon coding, document analysis, game development, and scientific reasoning. It serves a context window of 1M tokens. To use Hy4 Preview, set model to tencent/hy4-preview in the AI SDK: To use it in a coding agent, see the coding agents guide, then run vercel ai-gateway coding-agents setup to connect agents like Claude Code, Codex, OpenCode, Cursor, Pi, and more and select tencent/hy4-preview inside the agent. Try Hy4 Preview in the model playground. AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. You can view all language models available on AI Gateway. Read more ## 1Password signs OpenAI open letter calling for collective action on cyber defense DevFeed: [1Password signs OpenAI open letter calling for collective action on cyber defense]() Original publisher: [Read original article]() Author: info@1password.com (1Password) Published: 2026-08-28T00:00:00Z Content type: opinion Language: en Sources: [Blog on 1Password Blog]() Topics: [AI Bots](), [AI, ML & Data Engineering](), [cursor]() Tags: [agent](), [agentic](), [ai](), [audit-trail](), [aws](), [cloud-infrastructure](), [codex](), [collective](), [cursor](), [identity](), [openai](), [security](), [unified-access]() ### AI overview 1Password supports OpenAI's call for collective cyber defense, arguing that AI agents need least-privilege access, traceable identities, clear boundaries, and audit trails. ### Source excerpt As AI moves from answering questions to taking actions, the ecosystem around it will determine whether organizations can use it safely and with confidence. OpenAI's open letter on collective cyber defense warns that defenders have a limited window to strengthen security. It urges organizations to fix their highest-risk weaknesses, build least privilege and strong access controls, verify fixes, and make agentic identities traceable and accountable. The real work is building the ecosystem that lets them act safely and earn trust in production. That is why we continue working with OpenAI on trusted access for people and their agents. 1Password integrations with OpenAI, Codex, Anthropic Claude Code, Cursor, Kiro, Perplexity, and AWS Secrets Manager extend trusted access across development and cloud workflows. People should give agents access to key systems without exposing underlying credentials to the AI model. Cyber defense is a leadership responsibility. AI changes who and what can act inside the most sensitive systems, so identity security can no longer stop at human login. OpenAI is right to call for urgency, coordination, and fixes that organizations can verify without disrupting essential services. The standard is simple: every agent needs an identity, a boundary, and an audit trail." -Nancy Wang, Chief Technology Officer, 1Password Status quo security won't be enough Every security organization balances known weaknesses, technical debt, and limited time. The challenge for CISOs is deciding where to focus first and finding controls that reduce risk across the environment where AI is changing who and what can act inside an organization. Agents that work across browsers, repositories, terminals, cloud infrastructure, and production systems create a security challenge that begins before they take action. Standing access gives an agent more authority than a specific task requires and keeps it available after the task ends. If the agent is compromised or follows untru ## Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers DevFeed: [Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers]() Original publisher: [Read original article]() Author: Tom Aarsen Published: 2026-08-26T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [sentence-transformers](), [AI search](), [AI, ML & Data Engineering]() Tags: [community](), [datasets](), [embedding](), [embeddings](), [evaluation](), [guide](), [nlp](), [open-source](), [retrieval](), [sentence-transformers](), [training]() ### AI overview A tutorial on finetuning multi-vector embedding models with Sentence Transformers. It explains late-interaction token-level retrieval, training components, and domain-specific retrieval improvements. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Wire It, Run It, Deploy It: AI Workflows in Gradio DevFeed: [Wire It, Run It, Deploy It: AI Workflows in Gradio]() Original publisher: [Read original article]() Author: yuvraj sharma; Abubakar Abid Published: 2026-08-25T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [AI Chat](), [GPU]() Tags: [ai](), [gpu](), [gradio](), [hugging-face](), [inference-providers](), [llm](), [models](), [python](), [rest-api](), [spaces](), [tutorial](), [workflows]() ### AI overview A tutorial on building and deploying Gradio AI workflows as typed-node graphs, with runnable examples for image editing, generation, text-to-speech, dataset analysis, REST endpoints, and GPU-backed Python nodes. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## How Generative Recommenders Are Redefining RecSys at Scale DevFeed: [How Generative Recommenders Are Redefining RecSys at Scale]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [recommendation systems](), [AI, ML & Data Engineering](), [Training AI Models](), [Language models]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [data-center-cloud](), [developer-tools-techniques](), [embedding](), [featured](), [generative](), [gpu](), [inference](), [latency](), [llms](), [machine-learning](), [machine-learning-artificial-intelligence](), [recommenders-personalization](), [scale]() ### AI overview The article examines the shift toward generative recommender systems and the challenges of training and serving them at large scale. ### Source excerpt Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and... ## Detect vulnerabilities in LLM applications with Datadog's AI-native SAST DevFeed: [Detect vulnerabilities in LLM applications with Datadog's AI-native SAST]() Original publisher: [Read original article]() Author: Jon Green; Bahar Shah Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Vulnerabilities](), [Application Security](), [AI, ML & Data Engineering](), [ci]() Tags: [ai](), [ai-security](), [application-security](), [applications](), [ci](), [code-security](), [llm](), [security](), [threat-detection](), [vulnerabilities]() ### AI overview Datadog describes an AI-native SAST capability for finding LLM-specific application vulnerabilities, including prompt injection, excessive agency, and hidden-context exposure. It uses code context and data-flow reasoning, verifies findings, and surfaces results in pull requests and CI checks. ### Source excerpt Datadog Code Security's AI-native SAST helps detect vulnerabilities specific to the OWASP Top 10 for LLM Applications before they reach production. ## Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control DevFeed: [Post-Train NVIDIA Cosmos 3 Edge for On-Device Robot Control]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-19T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Cosmos](), [AI, ML & Data Engineering]() Tags: [agentic-ai-generative-ai](), [cosmos](), [deployment](), [edge](), [edge-computing](), [featured](), [foundation-models](), [inference](), [jetson](), [latency](), [nvidia](), [on-device](), [physical-ai](), [post-training](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [thor](), [tutorial]() ### AI overview A tutorial on post-training NVIDIA Cosmos 3 Edge as an on-device robot manipulation policy, serving it on Jetson Thor, running receding-horizon inference, and evaluating it in closed-loop simulation. ### Source excerpt Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for... ## 7 lessons for IT leaders on using observability to monitor AI applications DevFeed: [7 lessons for IT leaders on using observability to monitor AI applications]() Original publisher: [Read original article]() Author: Brad Quarry Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [ai observability](), [telemetry](), [Language models](), [AI, ML & Data Engineering]() Tags: [ai](), [applications](), [business](), [events](), [generative-ai](), [instrumentation](), [large-language-models](), [llm](), [monitor](), [observability](), [telemetry]() ### AI overview An Elastic IT case study argues that LLM observability should be included in AI application MVPs so teams can measure usage, time savings, and return on investment from the start. ### Source excerpt Discover the seven lessons we learned as we evolve our LLM observability practice to monitor and improve our AI applications. ## Confluent Cloud for Apache Flink: Engine for Mission-Critical, Real-Time Operational Systems and dbt/SQL-Native Home for Data Science and AI DevFeed: [Confluent Cloud for Apache Flink: Engine for Mission-Critical, Real-Time Operational Systems and dbt/SQL-Native Home for Data Science and AI]() Original publisher: [Read original article]() Author: Yashwanth Dasari Published: 2026-08-18T02:20:00Z Content type: release Language: en Sources: [Confluent: Data in motion]() Topics: [Confluent Cloud](), [apache-flink](), [stream-processing](), [Kafka](), [AI, ML & Data Engineering](), [Cloud](), [SDKs]() Tags: [ai](), [announcement](), [apache-flink](), [api](), [batch](), [confluent-cloud](), [data-science](), [developers](), [kafka](), [real-time]() ### AI overview Confluent announces an evolution of Confluent Cloud for Apache Flink that unifies mission-critical real-time operations with analytics and AI workflows. The release adds a serverless, co-designed Kafka and Flink platform, including the generally available Flink Table API in Java for code-first development. ### Source excerpt Flink now acts as a robust engine for developers through Table API, UDFs, and PTFs while offering a SQL-native, dbt-integrated platform for data science and AI teams. ## Data Engineering Weekly #283 DevFeed: [Data Engineering Weekly #283]() Original publisher: [Read original article]() Author: Ananth Packkildurai Published: 2026-08-17T02:59:40Z Content type: article Language: en Sources: [Data Engineering Weekly]() Topics: [data-engineering](), [AI, ML & Data Engineering](), [Data Quality](), [Microservice](), [Apache Spark](), [Kafka](), [Feature Engineering](), [audit trail](), [Protocol (disambiguation)]() Tags: [ai](), [anthropic](), [article](), [data-engineering](), [data-quality](), [engineering](), [feature-engineering](), [kafka](), [newsletter](), [pipelines](), [services](), [testing]() ### AI overview Data Engineering Weekly #283 is a newsletter covering data platform fundamentals, multiagent system coordination, payments platform data contracts, financial data quality, declarative data engineering, and cost-efficient export workloads. ### Source excerpt The Weekly Data Engineering Newsletter [Next page]()