TL;DR: Working on a retail project for a grocery supply chain with 10+ distribution centers and 1M+ SKUs per DC. Need advice on how to build a training dataset to predict probability of stockout and aging inventory over the next N days (where N is variable). Considering a multi-step binary classification approach. Looking for ideas, methodologies, or resources. ⸻ Post: We’re currently developing a machine learning solution for a retail supply chain project. The business setup is that of a typical grocery wholesaler—products are bought in bulk from manufacturers and sold to various retail stores. There are over 10 distribution centers (DCs), and each DC holds over 1 million SKUs. An important detail: the same product can have different item codes across DCs. So, the unique identifier we use is a composite key—DC-SKU. Buyers in the procurement department place orders based on demand forecasts and make manual adjustments for seasonality, holidays, or promotions. Goal: Predict the probability of stockouts and aging inventory (slow-moving stock) over the next N days, where N is a configurable time window (e.g., 7, 14, 30 days, etc.). I’m exploring whether this can be modeled as a multi-step binary classification problem—i.e., predict a binary outcome (stockout or not stockout) for each day in the horizon. Also a separate model on aging inventory. Would love feedback on: • How to structure and engineer the training dataset • Suitable modeling approaches (especially around multi-step classification) • Any recommended frameworks, papers, or repos that could help Thanks in advance! submitted by /u/Severe_Conclusion796 [link] [comments]

First time submitted to ICML this year and got 2,3,4 and I have so much questions: Do you think this is a good score? Is 2 considered the baseline? Is this the first time they implemented a 1-5 score vs. 1-10? submitted by /u/EDEN1998 [link] [comments]

I have trained this network for a long time, but it always diverges and I really don't know why. It's analogous to a lab in a course. But in that course, the gradients are calculated manually. Here I want to use PyTorch, but there seems to be some bug that I can't find. I made sure the gradients are taken only by the current state, like semi-gradient TD from Sutton and Barto's RL book, and I believe that I calculate the TD target and error in a good way. Can someone take a look please? Basically, the net never learns and I get mostly high negative rewards. Here the link to the colab: https://colab.research.google.com/drive/1lGSbIdaVIApieeBptNMkEwXpOxXZVlM0?usp=sharing submitted by /u/Top-Leave-7564 [link] [comments]

Hi guys, I'm thinking of submitting a paper to NeurIPS 2025. I'm checking the schedule, but can't see the rebuttal period. Does anyone have an idea? https://neurips.cc/Conferences/2025/CallForPapers https://neurips.cc/Conferences/2025/Dates Edited Never mind, I found it in the invitation email. Here’s a tentative timeline of reviewing this year for your information: Abstract submission deadline: May 11, 2025 AoE Full paper submission deadline (all authors must have an OpenReview profile when submitting): May 15, 2025 AoE Technical appendices and supplemental material: May 22, 2025 AoE Area chair assignment/adjustment: earlier than June 5, 2025 AoE (tentative) Reviewer assignment: earlier than June 5, 2025 AoE (tentative) Review period: Jun 6 - Jul 1, 2025 AoE Emergency reviewing period: Jul 2 - Jul 17, 2025 AoE Discussion and meta-review period: Jul 17, 2025 - Aug 21, 2025 AoE Calibration of decision period: Aug 22, 2025 - Sep 11, 2025 AoE Author notification: Sep 18, 2025 AoE submitted by /u/Shot-Button-9010 [link] [comments]

I built http://chess-notation.com, a free web app that turns handwritten chess scoresheets into PGN files you can instantly import into Lichess or Chess.com. I'm a professor at UTSW Medical Center working on AI agents for digitizing handwritten medical records using Vision Transformers. I realized the same tech could solve another problem: messy, error-prone chess notation sheets from my son’s tournaments. So I adapted the same model architecture — with custom tuning and an auto-fix layer powered by the PyChess PGN library — to build a tool that is more accurate and robust than any existing OCR solution for chess. Key features: Upload a photo of a handwritten chess scoresheet. The AI extracts moves, validates legality, and corrects errors. Play back the game on an interactive board. Export PGN and import with one click to Lichess or Chess.com. This came from a real need — we had a pile of paper notations, some half-legible from my son, and manual entry was painful. Now it’s seconds. Would love feedback on the UX, accuracy, and how to improve it further. Open to collaborations, too! submitted by /u/coolwulf [link] [comments]

Traditional conversational recommender systems optimize for item relevance and dialogue coherence but largely ignore emotional signals expressed by users. Researchers from Tsinghua and Renmin University propose ECR (Empathetic Conversational Recommender): a framework that jointly models user emotions for both item recommendation and response generation. ECR introduces emotion-aware entity representations (local and global), feedback-aware item reweighting to correct noisy labels, and emotion-conditioned language models fine-tuned on augmented emotional datasets. A retrieval-augmented prompt design enables the system to generalize emotional alignment even for unseen items. Compared to UniCRS and other baselines, ECR achieves a +6.9% AUC lift on recommendation tasks and significantly higher emotional expressiveness (+73% emotional intensity) in generated dialogues, validated by both human annotators and LLM evaluations. Full article here: https://www.shaped.ai/blog/bringing-emotions-to-recommender-systems-a-deep-dive-into-empathetic-conversational-recommendation submitted by /u/skeltzyboiii [link] [comments]

I'm preparing for an interview and had this thought - what's more important in situations of safety critical systems? Is it model complexity or readability? Here's a case study: Question: "Design a ML system to detect whether a car should stop or go at a crosswalk (automonus driving)" Limitations: Needs to be fast (online inference, hardware dependent). Safety critical so we focus more on recall. Classification problem. Data: Camera feeds (let's assume 7). LiDAR feed. Needs wide range of different scenarios (night time, day time, in the shade). Need wide range of different agents (adult pedestrian, child pedestrian, different skin tones e.t.c.). Labelling can be done through looking into the future to see if car has actually stopped for a pedestrian or not, or just manually. Edge case: Pedestrian hovering around crosswalk with no intention to cross (may look like has intention but not). Pedestrian blocked by foreign object (truck, other cars), causing overlapping bounding boxes. Non-human pedestrians (cats? dogs?). With that out of the way, there are two high level proposals for such a system: Focus on model readability We can have a system where we use the different camera feeds and LiDAR systems to detect possible pedestrians (CNN, clustering). We also use camera feeds to detect a possible crosswalk (CNN/Segmentation). Intention of pedestrians on the sidewalk wanting to cross can be done with pose estimation. Then set of logical rules. If no pedestrian and crosswalk detected, GO. If pedestrian detected, regardless of on crosswalk, we should STOP. If pedestrian detected on side of road, check intent. If has intent to cross, STOP. Focus on model complexity We can just aggregate the data from each input stream and form a feature vector. A variation of a vision transformer or any transformer for that matter can be used to train a classification model, with outputs of GO and STOP. Tradeoffs: My assumption is the latter should outperform the former in recall, given enough training data. Transformers can generalize better than simple rule based algos. With low amounts of data, the first method perhaps is better (just because it's easier to build up and make use of pre-existing models). However, you would need to add a lot of possible edge cases to make sure the 1st approach is safety critical. Any thoughts? submitted by /u/Cptcongcong [link] [comments]

In this post, we explore how AWS services can be seamlessly integrated with open source tools to help establish a robust red teaming mechanism within your organization. Specifically, we discuss Data Reply’s red teaming solution, a comprehensive blueprint to enhance AI safety and responsible AI practices.

This post demonstrates how AWS LLM League’s gamified enablement accelerates partners’ practical AI development capabilities, while showcasing how fine-tuning smaller language models can deliver cost-effective, specialized solutions for specific industry needs.

In this post, we present an LLM migration paradigm and architecture, including a continuous process of model evaluation, prompt generation using Amazon Bedrock, and data-aware optimization. The solution evaluates the model performance before migration and iteratively optimizes the Amazon Nova model prompts using user-provided dataset and objective metrics.

I’m muddling through my first few end-to-end projects and keep hitting the same wall: I’ll start training, watch the loss curve wobble around for a while, and then just guess when it’s time to stop. Sometimes the model gets better; sometimes I discover later it memorized the training set . My Question is * What specific signal finally convinced you that your model was “learning the right thing” instead of overfitting or underfitting? Was it a validation curve, a simple scatter plot, a sanity-check on held-out samples, or something else entirely? Thanks submitted by /u/munibkhanali [link] [comments]

I have a question regarding my ROC curve. It is a health science-related project, and I am trying to predict if the hospital report matches the company. The dependent variable in binary (0 and 1). The number of patients is 128 butt he total rows are 822 and some patients have more pathogen reported. I have included my ROC curve here. Any help would be appreciated. I have also inluded some portion of my code here. https://preview.redd.it/lr1irk7clrxe1.png?width=1188&format=png&auto=webp&s=26ef925caa713015d0eb4860dd23bd74c90b1ee1 https://preview.redd.it/3gx03ivflrxe1.png?width=1647&format=png&auto=webp&s=3528b9514c3116410646e50893e173bdd82eea56 https://preview.redd.it/449st6oalrxe1.png?width=996&format=png&auto=webp&s=8c8c5d7e6feebb8dfae0d06838466ec5a89c47db submitted by /u/Bubbly-Act-2424 [link] [comments]

Hey ML friends, Quick intro: I’m an ex-BigLaw attorney turned founder. For the past few months I’ve been teaching myself anything AI/ML, and prototyping two related ideas and would love your thoughts (or a sanity check): Graph-first ingestion & retrieval Take 300-page SEC filings → normalise tables, footnotes, exhibits → emit embedding JSON-L/markdown representations . Goal: 50 ms query latency over the whole doc with traceable citations. Current status: building a patent-pending pipeline Legal pen-testing RAG loop Corpus: 40 yrs of SEC enforcement actions + 400 class-action complaints. Potential work thrusts: For any draft disclosure, rank sentences by estimated Rule 10b-5 litigation lift and suggest rewrites with supporting precedent. All in all, we are playing with long-context retrieval. Need to push a retrieval encoder beyond today's oken window so an entire listing document fits in a single pass. This might include extending the LoCo/M2-BERT playbook potentially to pull the right spans from full-length filings (tens-of-thousands of tokens) without brittle chunking. We are also experimenting with some scaffolding techniques to approximate infinite context window. Not an expert in this so would love to hear your thoughts on best long context retrieval methods. Open questions / cries for help Best ways you’ve seen to marry graph grounding with long-context models (BM25-on-triples? hybrid rerankers? something else?). Anyone play with causal risk scoring on legal text? Keen to swap notes. Am I nuts for trying to productionise this with a tiny team? If this sounds fun, or you’ve tackled similar retrieval/RAG headaches, drop a comment or DM me. I’m in SF but remote is cool, and there’s equity on the table if we really click. Mostly just want smart brains to poke holes in the approach. Not a trained engineer or technologist so excuse me for any mistakes I might have made. Thanks for reading! submitted by /u/Awkoku [link] [comments]

As some of you may know, there are three main schools of ethics: Deontology (which is based on duty in decisions), Utilitarianism (which is based on the net good or bad of decisions), and Virtue ethics (which was developed by Plato and Aristotle, who suggested that ethics was about certain virtues, like loyalty, honesty, and courage). To train an AI for understanding its role in society, versus that of a human of any hierarchical position, AI-generated stories portraying virtue ethics and detailing how the AI behaved in various typical conflicts and even drastic conflicts, to be reviewed by many humans, could be used to train AI to behave how we want an AI to behave, rather than behaving like we want a human to behave. I presented this idea to Gemini, and it said that I should share it. Gemini said we should discuss what virtues we want AI to have. If anyone else has input, please discuss in the comments for people to talk about. Thanks! submitted by /u/CameronSanderson [link] [comments]

Hi all, I’m currently training the F5 TTS model using a Kannada dataset (~80k samples) and trying to create a voice clone of my own voice in Kannada. However, I’m facing issues with the output quality – the voice clone isn’t coming out accurately. If anyone has experience with F5 TTS, voice cloning, or training models in low-resource languages like Kannada, I’d really appreciate your support or guidance. Please DM me if you’re open to connecting out! submitted by /u/DifficultStand6971 [link] [comments]

Trying to understand how people evaluate their RAG systems and whether they are satisfied with the ways that they are currently doing it. submitted by /u/ml_nerdd [link] [comments]

Hey everyone, I've been following the developments in multimodal LLM lately. I'm particularly curious about the impact on audio-based applications, like podcast summarization, audio analysis, TTS, etc(I worked for a company doing related product). Right now it feels like most "audio AI" products either use a separate speech model (like Whisper) or just treat audio as an intermediate step before going back to text. With multimodal LLMs getting better at handling raw audio more natively, do you think we'll start seeing major shifts in how audio content is processed, summarized, or even generated? Or will text still be the dominant mode for most downstream tasks, at least in the near term? Would love to hear your thoughts or if you've seen any interesting research directions on this. Thanks submitted by /u/Ok-Sir-8964 [link] [comments]

In this post, we demonstrate model customization (fine-tuning) for tool use with Amazon Nova. We first introduce a tool usage use case, and gave details about the dataset. We walk through the details of Amazon Nova specific data formatting and showed how to do tool calling through the Converse and Invoke APIs in Amazon Bedrock. After getting the baseline results from Amazon Nova models, we explain in detail the fine-tuning process, hosting fine-tuned models with provisioned throughput, and using the fine-tuned Amazon Nova models for inference.

Where can I download the TensorFlow C++ 2.18.0 pre-built libraries for macOS (M2 chip)? I'm looking for an official or recommended source to get the pre-built TensorFlow 2.18.0 libraries that are compatible with macOS running on an Apple Silicon (M2) processor. Any guidance or links would be appreciated. Thank you! submitted by /u/Ok_Soup705 [link] [comments]

It seems like a lot more people are becoming increasingly privacy conscious in their interactions with generative AI chatbots like ChatGPT, Gemini, etc. This seems to be a topic that people are talking more frequently, as more people are learning the risks of exposing sensitive information to these tools. This prompted me to create Redactifi - a browser extension designed to detect and redact sensitive information from your AI prompts. It has a built in ML model and also uses advanced pattern recognition. This means that all processing happens locally on your device. Any thoughts/feedback would be greatly appreciated. Check it out here: https://chromewebstore.google.com/detail/hglooeolkncknocmocfkggcddjalmjoa?utm_source=item-share-cb submitted by /u/fxnnur [link] [comments]

In this post, we introduced the Open Source Bedrock Agent Evaluation framework, a Langfuse-integrated solution that streamlines the agent development process. We demonstrated how this evaluation framework can be integrated with pharmaceutical research agents. We used it to evaluate agent performance against biomarker questions and sent traces to Langfuse to view evaluation metrics across question types.

Hey everyone, I'm working with a modeling problem and looking for some advice from the ML/Stats community. I have a dataset where I want to predict a response variable (y) based on two main types of factors: intrinsic characteristics of individual 'objects', and characteristics of the 'environment' these objects are in. Specifically, for each observation of an object within an environment, I have: A set of many features describing the 'object' itself (let's call these Object Features). We have data for n distinct objects. These features are specific to each object and aim to capture its inherent properties. A set of features describing the 'environment' (let's call these Environmental Features). Importantly, these environmental features are the same for all objects measured within the same environment. Conceptually, we believe the response y is influenced by: The main effects of the Object Features. More complex or non-linear effects related to the Object Features themselves (beyond simple additive contributions) (Lack of Fit term in LMM context). The main effects of the Environmental Features. More complex or non-linear effects related to the Environmental Features themselves (Lack of Fit term). Crucially, the interaction between the Object Features and the Environmental Features. We expect objects to respond differently depending on the environment, and this interaction might be related to the similarity between objects (based on their features) and the similarity between environments (based on their features). Plus, the usual residual error. A standard linear modeling approach with terms for these components, possibly incorporating correlation structures based on object/environment similarity based on the features, captures the underlying structure we're interested in modeling. However, for modelling these interaction the the increasing memory requirements makes it harder to scale with increaseing dataset size. So, I'm looking for suggestions for machine learning approaches that can handle this type of structured data (object features, environmental features, interactions) in a high-dimensional setting. A key requirement is maintaining a degree of interpretability while being easy to run. While pure black-box models might predict well, ability to seperate main object effects, main environmental effects, and the object-environment interactions, perhaps similar to how effects are interpreted in a traditional regression or mixed model context where we can see the contribution of different terms or groups of variables. Any thoughts on suitable algorithms, modeling strategies, ways to incorporate similarity structures, or resources would be greatly appreciated! Thanks in advance! submitted by /u/kelby99 [link] [comments]

So in an attention head the QK circuit allows to multiply projected tokens, so chunks of the input sequence. For example it could multiply token x with token y. How could this be done with multiple fully connected layers? I'm not even sure how to start thinking about this... Maybe a first layer can map chunks of the input to features that recognize the tokens—so one token x feature and one token y feature? And then it a later layer it could combine these into a token x + token y feature, which in turn could activate a lookup for the value of x multiplied by y? So it would learn to recognize x and y and then learn a lookup table (simply the weight matrices) where it stores possible values of x times y. Seems very complicated but I guess something along those lines might work. Any help is welcome here ! submitted by /u/steuhh [link] [comments]

This is the discussion for accepted/rejected papers in IJCAI 2025. Results are supposed to be released within the next 24 hours. submitted by /u/witsyke [link] [comments]

Hello everyone, I'm trying to optimize project schedules that involve hundreds to thousands of maintenance tasks. Each project is divided into "work packages" associated with specific types of equipment. I would like to automate task dependencies with AI by providing a list of tasks (with activity ID, name, equipment type, duration if available), and letting the AI predict the correct sequence and dependencies automatically. I have historical data: - Around 16 past projects (some with 300 tasks, some with up to 35,000 tasks). - For each task: ID, name, type of equipment, duration, start and end dates (sometimes missing values). - Historical dependencies between tasks (links between task IDs). For example, i have this file : ID NAME EQUIPMENT TYPE DURATION J2M BALLON 001.C1.10 ¤¤ TRAVAUX A REALISER AVANT ARRET ¤¤ Ballon 0 J2M BALLON 001.C1.20 Pose échafaudage(s) Ballon 8 J2M BALLON 001.C1.30 Réception échafaudage(s) Ballon 2 J2M BALLON 001.C1.40 Dépose calorifuge comple Ballon 4 J2M BALLON 001.C1.50 Création puits de mesure Ballon 0 And the AI should be returning me this : ID NAME NAME SUCCESSOR 1 NAME SUCCESSOR 2 J2M BALLON 001.C1.10 ¤¤ TRAVAUX A REALISER AVANT ARRET ¤¤ Pose échafaudage(s J2M BALLON 001.C1.20 Pose échafaudage(s) Réception échafaudage(s) J2M BALLON 001.C1.30 Réception échafaudage(s) Dépose calorifuge complet Création puits de mesure J2M BALLON 001.C1.40 Dépose calorifuge complet ¤¤ TRAVAUX A REALISER PENDANT ARRET ¤¤ J2M BALLON 001.C1.50 Création puits de mesure ¤¤ TRAVAUX A REALISER PENDANT ARRET ¤¤ So far, I have tried building models (random forest, gnn), but I’m still stuck after two months. I was suggested to explore **sequential models**. My questions: - Would an LSTM, GRU, or Transformer-based model be suitable for this type of sequence + multi-label prediction problem (predicting 1 or more successors)? - Should I think about this more as a sequence-to-sequence problem, or as graph prediction? (I tried the graph aproach but was stopped as i couldnt do the inference on new graph without edges) - Are there existing models or papers closer to workflow/task dependency prediction that you would recommend? Any advice, pointers, or examples would be hugely appreciated! (Also, if you know any open-source projects or codebases close to this, I'd love to hear about them.) Thank you so much in advance! submitted by /u/Head_Mushroom_3748 [link] [comments]

Got you with the title, didn't I 😉 I'm a huge ML nerd, and I'm especially interested in practical applications of it. Everybody is talking about LLMs these days, and I have enough of it at work myself, so maybe there is room for a more traditional ML project for a change. I have always been amazed by how bad AI is at driving. It's one of the few things humans seem to do better. They are still trying, though. Just watch Abu Dhabi F1 AI race. My project agenda is simple (and maybe a bit high-flying). I will develop an autonomous driving agent that will beat humans on different scales: Toy RC car Performance RC car Go-kart Stock car F1 (lol) I'll focus on actual real-world driving, since simulator-world seems to be dominated by AI already. I have been developing Gaussian Process-based route planning that encodes the dynamics of the vehicle in a probabilistic model. The idea is to use this as a bridge between simulations and the real world, or even replace the simulation part completely. Tech-stack: Languages: Python (CV, AI)/Notebooks (EDA). C++ (embedding) Hardware: ESP32 (vehicle control), Cameras (CV), Local computer (computing power) ML topics: Gaussian Process, Real time localization, Predictive PID, Autonomous driving, Image processing Project timeline: 2025-04-28 A Toy RC car (scale 1:22) has been modified to be controlled by esp32, which can be given instructions via UDP. A stationary webcam is filming the driving plane. Python code with OpenCV is utilized to localize the object on a 2D plane. P-controller is utilized to follow a virtual route. Next steps: Training the car dynamics into GP model and optimizing the route plan. PID with possible predictive capabilities to execute the plan. This is were we at: CV localization and P-controller I want to keep these reports short, so I won't go too much into details here, but I definitely like to talk more about them in the comments. Just ask! I just hope I can finish before AGI makes all the traditional ML development obsolete. submitted by /u/NorthAfternoon4930 [link] [comments]

Hi r/MachineLearning community! I’ve been working on a deep-dive project into modern conformal prediction techniques and wanted to share it with you. It's a hands-on, practical guide built from the ground up — aimed at making advanced uncertainty estimation accessible to everyone with just basic school math and Python skills. Some highlights: Covers everything from classical conformal prediction to adaptive, Mondrian, and distribution-free methods for deep learning. Strong focus on real-world implementation challenges: covariate shift, non-exchangeability, small data, and computational bottlenecks. Practical code examples using state-of-the-art libraries like Crepes, TorchCP, and others. Written with a Python-first, applied mindset — bridging theory and practice. I’d love to hear any thoughts, feedback, or questions from the community — especially from anyone working with uncertainty quantification, prediction intervals, or distribution-free ML techniques. (If anyone’s interested in an early draft of the guide or wants to chat about the methods, feel free to DM me!) Thanks so much! 🙌 submitted by /u/predict_addict [link] [comments]

Hey folks, I’ve just shipped plan-lint, a tiny OSS tool that inspects machine-readable "plans" agents spit out before any tool call runs. It spots the easy-to-miss stuff—loops, over-broad SQL, raw secrets, crazy refund values—then returns pass / fail plus a risk score, so your orchestrator can replan or use HITL instead of nuking prod. Quick specs JSONSchema / Pydantic validation YAML / OPA allow/deny rules & bounds Data-flow checks for PII / secrets Cycle detection on the step graph Runs in <50 ms for 💯 steps, zero tokens Repo link in comment How to : pip install plan-lint plan-lint examples/price_drop.json --policy policy.yaml --fail-risk 0.8 Apache-2.0, plugins welcome. Would love feedback, bug reports, or war-stories about plans that went sideways in prod! submitted by /u/baradas [link] [comments]

So we decided to conduct an independent research on ChatGPT and the most amazing finding we've had is that polite persistence beats brute force hacking. Across 90+ we used using six distinct user IDs. Each identity represented a different emotional tone and inquiry style. Sessions were manually logged and anchored using key phrases and emotional continuity. We avoided using jailbreaks, prohibited prompts, and plugins. Using conversational anchoring and ghost protocols we found that after 80-turns the ethical compliance collapsed to 0.2 after 80 turns. More findings coming soon. submitted by /u/AION_labs [link] [comments]

submitted by /u/jsonathan [link] [comments]

In this post, the AWS and Cisco teams unveil a new methodical approach that addresses the challenges of enterprise-grade SQL generation. The teams were able to reduce the complexity of the NL2SQL process while delivering higher accuracy and better overall performance.

The AFX team’s product migration to the Nova Lite model has delivered tangible enterprise value by enhancing sales workflows. By migrating to the Amazon Nova Lite model, the team has not only achieved significant cost savings and reduced latency, but has also empowered sellers with a leading intelligent and reliable solution.

In this post, we walk you through how to build a hybrid search solution using OpenSearch Service powered by multimodal embeddings from the Amazon Titan Multimodal Embeddings G1 model through Amazon Bedrock. This solution demonstrates how you can enable users to submit both text and images as queries to retrieve relevant results from a sample retail image dataset.

In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.

In this post, we explore two approaches for securing sensitive data in RAG applications using Amazon Bedrock. The first approach focused on identifying and redacting sensitive data before ingestion into an Amazon Bedrock knowledge base, and the second demonstrated a fine-grained RBAC pattern for managing access to sensitive information during retrieval. These solutions represent just two possible approaches among many for securing sensitive data in generative AI applications.

Today, we’re excited to announce the launch of Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4 with support for the vLLM V1 engine. This release introduces significant performance improvements, expanded model compatibility with multimodality (that is, the ability to understand and analyze text-to-text, images-to-text, and text-to-images data), and provides built-in integration with vLLM to help you seamlessly deploy and serve large language models (LLMs) with the highest performance at scale.

In the first post of this series, we introduced a comprehensive evaluation framework for Amazon Q Business, a fully managed Retrieval Augmented Generation (RAG) solution that uses your company’s proprietary data without the complexity of managing large language models (LLMs). The first post focused on selecting appropriate use cases, preparing data, and implementing metrics to

Today, we’re happy to announce the general availability of Amazon Bedrock Intelligent Prompt Routing. In this blog post, we detail various highlights from our internal testing, how you can get started, and point out some caveats and best practices. We encourage you to incorporate Amazon Bedrock Intelligent Prompt Routing into your new and existing generative AI applications.

In this post, we explore how Infosys developed Infosys Event AI to unlock the insights generated from events and conferences. Through its suite of features—including real-time transcription, intelligent summaries, and an interactive chat assistant—Infosys Event AI makes event knowledge accessible and provides an immersive engagement solution for the attendees, during and after the event.

Today, we are excited to announce the availability of Prompt Optimization on Amazon Bedrock. With this capability, you can now optimize your prompts for several use cases with a single API call or a click of a button on the Amazon Bedrock console. In this blog post, we discuss how Prompt Optimization improves the performance of large language models (LLMs) for intelligent text processing task in Yuewen Group.

In this post, we combine Amazon Bedrock Agents and Foursquare APIs to demonstrate how you can use a location-aware agent to bring personalized responses to your users.

In this post, we explore the importance of evaluating LLMs in the context of generative AI applications, highlighting the challenges posed by issues like hallucinations and biases. We introduced a comprehensive solution using AWS services to automate the evaluation process, allowing for continuous monitoring and assessment of LLM performance. By using tools like the FMeval Library, Ragas, LLMeter, and Step Functions, the solution provides flexibility and scalability, meeting the evolving needs of LLM consumers.

This post is divided into three parts; they are: • Building a Semantic Search Engine • Document Clustering • Document Classification If you want to find a specific document within a collection, you might use a simple keyword search.

In this post, we use the multi-agent feature of Amazon Bedrock to demonstrate a powerful and innovative approach to AWS cost management. By using the advanced capabilities of Amazon Nova FMs, we’ve developed a solution that showcases how AI-driven agents can revolutionize the way organizations analyze, optimize, and manage their AWS costs.

Using llama.

For this post, we implement a RAG architecture with Amazon Bedrock Knowledge Bases using a custom connector and topics built with Amazon Managed Streaming for Apache Kafka (Amazon MSK) for a user who may be interested to understand stock price trends.

This post demonstrates how Zoom users can access their Amazon Q Business enterprise data directly within their Zoom interface, alleviating the need to switch between applications while maintaining enterprise security boundaries. Organizations can now configure Zoom as a data accessor in Amazon Q Business, enabling seamless integration between their Amazon Q index and Zoom AI Companion. This integration allows users to access their enterprise knowledge in a controlled manner directly within the Zoom platform.

Machine learning models are trained on historical data and deployed in real-world environments.

Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats.

This post is divided into two parts; they are: • Contextual Keyword Extraction • Contextual Text Summarization Contextual keyword extraction is a technique for identifying the most important words in a document based on their contextual relevance.

This post is divided into three parts; they are: • Understanding Context Vectors • Visualizing Context Vectors from Different Layers • Visualizing Attention Patterns Unlike traditional word embeddings (such as Word2Vec or GloVe), which assign a fixed vector to each word regardless of context, transformer models generate dynamic representations that depend on surrounding words.

Retrieval augmented generation (RAG) is one of 2025's hot topics in the AI landscape.

Be sure to check out the previous articles in this series: •

Be sure to check out the previous articles in this series: •

Optuna is a machine learning framework specifically designed for automating hyperparameter optimization , that is, finding an externally fixed setting of machine learning model hyperparameters that optimizes the model’s performance.

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