Hi everyone, I’m working on PINNs and PI-DeepONet with multiple outputs, and my loss function only includes residuals. No data loss. The issue is that one of the outputs is much smaller in magnitude than the others. For example, in one test case, y3 is 100x smaller than y1 and y2. In another test case, y1 is 1000x smaller. I tried assigning different weights to each residual in the loss function, it didn’t help. Also tried normalizing by dividing each residual by its largest value, again, too specific and doesn’t generalize well across cases. Any ideas on how to handle this more generally? Would appreciate any advice. submitted by /u/PurpleCardiologist11 [link] [comments]

A recent symbolic compression pipeline I made allowed a 7B parameter language model to be trained and run on just 8GB of VRAM (RTX 4060). The setup used symbolic tokenization, modular encoding layers, and a lightweight fallback system for inference. Key metrics: Steps/sec: 0.069 Samples/sec: 0.276 Total FLOPs: 87.2 trillion Iterations/sec: ~14.5 Final loss: 0.1405 Hardware: 32GB RAM, 20-core CPU, RTX 4060 OS: Windows 10, Python 3.12 The compression stack preserved model quality while drastically reducing compute demands. Inference performance remained near full despite the constrained VRAM. Symbolic abstraction seems promising as a way to make large-scale models accessible on standard consumer hardware. Curious what others think about this direction. submitted by /u/AlphaCalamity [link] [comments]

I am trying to finetune whisper for live translation. My input will be audio from lang-A and the output will be in English text. I created a dataset using indicTrans2 and google fleurs. It adds a translation column to fleurs which is in English. I am trying to finetune the whisper small model, but it starts hellucinating and the WER does not decrease much. I can made the link to my dataset available if you are interested. Anyone has experience in such project? submitted by /u/Internal_Assist4004 [link] [comments]

Hey guys, This is the third time I’ve had to work with a dataset like this, and I’m hitting a wall again. I'm getting a consistent 70% accuracy no matter what model I use. It feels like the problem is with the data itself, but I have no idea how to fix it when the dataset is "final" and can’t be changed. Here’s what I’ve done so far in terms of preprocessing: Removed invalid entries Removed outliers Checked and handled missing values Removed duplicates Standardized the numeric features using StandardScaler Binarized the categorical data into numerical values Split the data into training and test sets Despite all that, the accuracy stays around 70%. Every model I try—logistic regression, decision tree, random forest, etc.—gives nearly the same result. It’s super frustrating. Here are the features in the dataset: id: unique identifier for each patient age: in days gender: 1 for women, 2 for men height: in cm weight: in kg ap_hi: systolic blood pressure ap_lo: diastolic blood pressure cholesterol: 1 (normal), 2 (above normal), 3 (well above normal) gluc: 1 (normal), 2 (above normal), 3 (well above normal) smoke: binary alco: binary (alcohol consumption) active: binary (physical activity) cardio: binary target (presence of cardiovascular disease) I'm trying to predict cardio (1 and 0) using a pretty bad dataset. This is a challenge I was given, and the goal is to hit 90% accuracy, but it's been a struggle so far. If you’ve ever worked with similar medical or health datasets, how do you approach this kind of problem? Any advice or pointers would be hugely appreciated. submitted by /u/CogniLord [link] [comments]

Sometimes in ML papers I see architectures being proposed which have matrix multiplications in sequence that could be collapsed into a single matrix. E.g. when a feature vector x is first multiplied by learnable matrix A and then by another learnable matrix B, without any nonlinearity in between. Take for example the attention mechanism in the Transformer architecture, where one first multiplies by W_V and then by W_O. Has it been researched whether there is any sort of advantage to having two learnable matrices instead of one? Aside from the computational and storage benefits of being able to factor a large n x n matrix into an n x d and a d x n matrix, of course. (which, btw, is not the case in the given example of the Transformer attention mechanism). submitted by /u/DescriptionClassic47 [link] [comments]

I’ve been given this project where I have to put a camera on a drone and somehow make it detect fires. The thing is, I have no idea how to approach the AI part. I’ve never done anything with computer vision, image processing, or machine learning before. I’ve got like 7–8 weeks to figure this out. If anyone could point me in the right direction — maybe recommend a good tool or platform to use, some tutorials or videos, or even just explain how the whole process works — I’d really appreciate it. I’m not asking for someone to do it for me, I just want to understand what I’m supposed to be learning and using here. Thanks in advance. submitted by /u/Flimisi69 [link] [comments]

Hey everyone, I just got an email saying no authors are registered for my accepted CVPR 2025 paper and that I need to register by today. However I did register weeks ago and my account shows I’ve already paid and completed registration. Has anyone else had this problem or/and know how to fix this? I contacted the organisers but received no response for now. submitted by /u/Training-Adeptness57 [link] [comments]

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 everyone, I am currently conducting research for my master’s thesis at Maastricht University (Business Intelligence and Smart Services), focusing on how organizations operationalize fairness, accountability, and transparency in Generative AI applications. I am looking for professionals who work with or manage AI systems to complete a short survey (15–20 minutes). Participation is anonymous, and the results will contribute to academic research on real-world AI ethics practices. 👉 Survey link: https://maastrichtuniversity.eu.qualtrics.com/jfe/form/SV_bNS6Fmb4u8Det26 Your input would be incredibly valuable, and I would greatly appreciate your participation! Feel free to share the link with colleagues who work in AI as well. Thank you very much for your support! – Hilda Master’s student | Maastricht University submitted by /u/AutomaticAgent2756 [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]

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.

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.

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]

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]

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]

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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