I am doing a project on prediction on the water quality prediction. To train the ml model we need to have x(independent variables) and y(dependent variable) values. I am using the weighted arethamatic water quality index to calculate the value of y from the x using some mathematical equations, Now after calculating the y value I am training the ml models on x and y values. My question is that is ml models worth applying are they doing some add-on to find information? question highlights an important consideration in using ML models for water quality prediction when the Weighted Arithmetic Water Quality Index (WAWQI) is already available I feel that the same thing that is done by the ml model can also be done by calculating the wawqi value for the test data and then tell from the wawqi value that the water is good or not. so why ml models need to be used ? And I have seen some papers doing the same thing but cannot understand why ? helpful inputs are appreciated. submitted by /u/Silver_Bison_4987 [link] [comments]

Hello everyone! I am an international student in the US in my 4th semester at college studying Data Science (focus on Machine Learning), and my dream is to become a Machine Learning Engineer. I switched my major to Data Science after my first year studying CS, so I am basically a newbie to this field. I found out I really love this major, and I have taken some Data Science classes at school along with some Machine Learning courses on my own (Machine Learning and Deep Learning Specializations by Andrew Ng, Machine Learning in Production by DeepLearning.ai, AWS Fundamentals Specialization,...). Besides, I have some personal projects on GitHub. However, I am still struggling to find an internship and figure out what else I need to study/improve to be able to get the job. Through this, I hope everyone who has knowledge/expertise in this field could share with me a good roadmap to get a job/stand out as an aspiring ML engineer. Any supports/advices is deeply appreciated. Thank you! submitted by /u/Thomas_ng_31 [link] [comments]

Heard from two independent sources at MSFT (one close to Sebastien Bubeck) about the upcoming Phi-3 models: Three different sized models (up to 14B) Again, mostly synthetic and LLM-augmented training data Apparently some upscaling techniques on the training side No more Apache 2 but more restrictive license (similar to llama3) Mixtral level performance with much fewer parameters I wanted to see if anyone has more insider information about the models. submitted by /u/yusuf-bengio [link] [comments]

For the dozen or so models I have seen so far, the amount of data (after all cleaning and filtering) that is used to train and validate is only few thousands of records (max I have seen is 500K) . Is this common? submitted by /u/mdghouse1986 [link] [comments]

Hello ML community, I wanted to get a pulse on how people are evaluating LLM accuracy at their workplace or for personal usage. Everyone wants to adopt LLMs, but the use cases I've seen so far are all similar in their setup. This is a highly complex scenario, so I thought this'd be a good opportunity for like-minded folks to connect and share thoughts. This article provides a decent primer on some LLM evaluation methods currently used in industry. Articles on the web center around the discussion of the following metrics: ROUGE scores, BLEU / BLEURT scores, MAE, etc. Natural Language Inference (Entailment) scores. Building a ground-truth dataset and comparing the answer's relevancy, the context's relevancy, and groundedness (whether the answer uses the context or not). Chain of Thought and basic self-evaluation to detect and moderate hallucination, sentiment, tone, stereotyping, and inaccurate logical assumptions. User surveys and opportunities for user feedback like thumbs up/down UIs. There are some recent libraries like Trulens, DeepEval, and RAGAS that aim to bridge the issues present in LLM evaluation. But I wanted to understand the following: Is anyone trying any novel methodology approaches for assessing LLM accuracy? Has anyone seen any niche papers recently released that aim to provide comprehensive evals of chatbot accuracy especially without ground truth datasets? Most papers are from late 2023 and use a fairly rigid set of evaluation metrics. Appropriate context: I work in Higher Education and EdTech Data Science. Therefore, accuracy assessment is paramount for student-facing and staff-facing LLM use cases. submitted by /u/Varunshou [link] [comments]

I'm looking for any public available dataset that contains curved text lines (preferably one per image), like those from "Alignment of Curved Text Strings for Enhanced OCR Readability". submitted by /u/Neural_Prodigy [link] [comments]

I've been working on Hybrid Search using Weaviate. I use OpenAI's latest embeddings model, and then some other stuff. So, the problem is, for some queries, I would like to focus on specific properties while for others, I would like to focus on other properties more. I also have 2 axes on which I want to rank the recommendations - relevance and excellence. Relevance would be how relevant they are to my search, and excellence would be how excellent the "document" is based on some score that I give it. So far, the things I've tried are: Cohere reranking. I saw that v3 reranking gave marginally better results for short queries than the "Hybrid Score" for Weaviate For shorter queries, I do more of a keyword search and for longer queries, more of a semantic search (shifting the alpha value based on word count) Assigning weights for keyword search in Weaviate Tried using a linear combination of my in house eval and relevancy (reranked score/hybrid score) and sorted based on that. This didnt really provide satisfactory results at all. Are there any suggestions based on which I could try improving the Search results? I want: To be able to "understand" what the query is for, and focus on that property more in my vector DB schema for the search For common queries, I want to be able to surface more "excellent" recommendations, as if its common, rather than focusing on very very relevant stuff, if it meets a certain level of relevancy and then is really excellent, that is the best way to go and looks really good in search results For larger/more niche queries, focus on the relevancy a lot more I think fine tuning Cohere's reranking model might be an option here? How do I factor in the excellence? What are my options here, and where do I go from here? Also, I've been checking distributions of scores that are returned from Weaviate upon the hybrid search, and I see that in lot of cases, if, say, I return the top 800 people from my query, most of them (~500-700) fall in the range of <0.3 vector/keyword scores or they do not have keyword/vector scores at all in which case one score is just 0. What are my options here? submitted by /u/DarthLoki79 [link] [comments]

Basically the title sums it up but why do we use a "worse" model to calculate image distance? I would imagine a ViT model is better able to capture semantic differences between images? submitted by /u/Karan1213 [link] [comments]

By instance level data I mean that, instead of just reporting aggregated metrics like mean accuracy, if I had one dataset and evaluated two models with it, the data would look like this: Model 1, Image 1: CORRECT Model 1, Image 2: FAIL Model 2, Image 1: FAIL Model 2, Image 2: FAIL For language models and text-to-text or text-to-image tasks the HELM initiative (https://crfm.stanford.edu/helm/lite/latest/) appeared. Is there anything similar for other domains like computer vision or robotics? submitted by /u/RF-Enthusiast [link] [comments]

Hey everyone! I've been tinkering around with OpenAI for text embedding. Full disclosure, I'm not a pro ML Engineer yet, but I'm eager to soak up knowledge like a sponge! I've been playing with OpenAI to embed text, and I'm trying to figure out how to handle text files of all shapes and sizes. Here's where I could use a hand: Any tips for a newbie like me trying to make the most of OpenAI for text embedding, especially with different-sized documents? How do I make sure I'm not losing the plot (literally!) when I'm working with varying document lengths? Hit me up with your thoughts, experiences, or even just some good vibes! submitted by /u/Affectionate-Olive80 [link] [comments]

Hey there, Redditors! I'm back with the latest installment on creating deterministic LLM memory. If you've been following along, you know I'm on a mission to move beyond the "thin OpenAI wrapper" trend and tackle the challenges of building robust LLM memory. That's why we built cognee, a python library to process documents and build knowledge graphs on top of them. After a few weeks of work, we integrated DSPy and extended cognee. Here is brief overview of the logic: Architecture overview We aim to understand: Have you tried building knowledge graphs with other tools before? If so, what were the biggest obstacles? How would you approach semantic linking of documents without knowledge graphs? Remember to give this post an upvote if you found it insightful! And also star our Github repo submitted by /u/Snoo-bedooo [link] [comments]

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. With SageMaker HyperPod, you can train FMs for weeks and months without disruption. Typically, HyperPod clusters are used by multiple users: machine learning (ML) researchers, software engineers, data scientists,

Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. A Gartner, Inc. survey revealed that 87 percent of business leaders expect to increase their organization’s investment in sustainability over the next years. This post serves as a starting point for any executive seeking to navigate the intersection of generative

A small description of my project; I need to make a model that can predict the next day’s hourly power consumption of a heat pump, and a stand-alone program that reads the needed online weather data for making the prediction. The pump data is on hourly base starting from March 1st 2023 until mid April 2024. There are two pairs of files for the pump, one named "heat pump" and the other "AUX". The first one contains the power consumed by the pump itself, and the second one measures the additional electrical heating element that kicks in when it is too cold for the heat pump alone. I was wondering what the right procedure would be in dealing with the “AUX” data when predicting the consumption of the heat pump (if it should be considered at all). Moreover, the heat pump was turned off in the summer period, roughly from mid May to some day in October. Stand-by consumption is roughly 6W. However, during heating season, the system sometimes goes to stand-by with the same consumption on its own. Can this data be used as is or should the period where the pump was put into stand-by manually be removed, leading to 2 cut timeseries (I.e. one frame from March till May, 2023 and one from October 2023 till April 2024). If the latter choice would be the correct option, this would lead to my next question; should the 2 timeseries be modelled separately or do I concatenate them and model it as one whole. Last but not least, I will be training and evaluating the dataset as temporal data. Is this approach appropriate and are there any other things I need to take into consideration? Any papers etc. that would be usefull to my projects will be highly appreciated. ​ ​ submitted by /u/Stunning-Front-7954 [link] [comments]

Hi all, I implemented a vision language model consisting of an image encoder, a multimodal projection module and a decoder language model in pure pytorch. Think of this as a simplified version of what you see in GPT-4 or Claude 3 in terms of vision capabilities demonstrated by a language model (think moondream 2 or LLaVA when it comes to Open Source Models). The name ‘seemore’ is my way of paying homage to Andrej Karpathy’s project ‘makemore’ because here I use a character level autoregressive language model much like in his nanoGPT/ makemore implementation. My goal is for this to be a hackable implementation that people use to understand how this really works and improve upon. I foresee more and more of these models coming out throughout the year. The blog with a link to the repo is here: https://huggingface.co/blog/AviSoori1x/seemore-vision-language-model Hope this is helpful or at least interesting! (posted this on LocalLlama but figured this is just as applicable to ML in general) submitted by /u/avi1x [link] [comments]

Full Blog Post Meta released Llama-3 only three days ago, and it already feels like the inflection point when open source models finally closed the gap with proprietary models. The initial benchmarks show that Llama-3 70B comes pretty close to GPT-4 in many tasks: The official Meta page only shows that Llama-3 outperforms Gemini 1.5 and Claude Sonnet. Artificial Analysis shows that Llama-3 is in-between Gemini-1.5 and Opus/GPT-4 for quality. On LMSYS Chatbot Arena Leaderboard, Llama-3 is ranked #5 while current GPT-4 models and Claude Opus are still tied at #1. The even more powerful Llama-3 400B+ model is still in training and is likely to surpass GPT-4 and Opus once released. Meta vs OpenAI Some speculate that Meta's goal from the start was to target OpenAI with a "scorched earth" approach by releasing powerful open models to disrupt the competitive landscape and avoid being left behind in the AI race. Meta can likely outspend OpenAI on compute and talent: OpenAI makes an estimated revenue of $2B and is likely unprofitable. Meta generated a revenue of $134B and profits of $39B in 2023. Meta's compute resources likely outrank OpenAI by now. Open source likely attracts better talent and researchers. One possible outcome could be the acquisition of OpenAI by Microsoft to catch up with Meta. Google is also making moves into the open model space and has similar capabilities to Meta. It will be interesting to see where they fit in. The Winners: Developers and AI Product Startups I recently wrote about the excitement of building an AI startup right now, as your product automatically improves with each major model advancement. With the release of Llama-3, the opportunities for developers are even greater: No more vendor lock-in. Instead of just wrapping proprietary API endpoints, developers can now integrate AI deeply into their products in a very cost-effective and performant way. There are already over 800 llama-3 models variations on Hugging Face, and it looks like everyone will be able to fine-tune for their us-cases, languages, or industry. Faster, cheaper hardware: Groq can now generate 800 llama-3 tokens per second at a small fraction of the GPT costs. Near-instant LLM responses at low prices are on the horizon. Open source multimodal models for vision and video still have to catch up, but I expect this to happen very soon. The release of Llama-3 marks a significant milestone in the democratization of AI, but it's probably too early to declare the death of proprietary models. Who knows, maybe GPT-5 will surprise us all and surpass our imaginations of what transformer models can do. These are definitely super exciting times to build in the AI space! submitted by /u/madredditscientist [link] [comments]

I am working on a recommendation system on a user item interaction matrix based on implicit feedback (binary), and I have positive observed interactions and a very small amount of negative observed interactions. For both user and item there are vector features available as embeddings. Are there any methods that I can perform negative sampling through the negative observations? I have heard of methods like contrastive learning but not aware of ways to integrate existing negative observations. submitted by /u/No_Carpenter_9469 [link] [comments]

The researchers segmented the sequence and added special memory tokens to the input: memory states from the output of the previous segment became inputs for the next one. Thus, a whole transformer acts as a recurrent cell, and memory serves as the recurrent state of the network. This approach was called Recurrent Memory Transformer (RMT). The authors augmented small transformer models like BERT and GPT-2 with this memory and tested them on various question-answering tasks where facts needed for answering are somewhere in the text. It was found that using recurrent memory significantly increases the length of the input sequence while maintaining satisfactory neural network performance accuracy. In their experiments, scientists were able to extend this value to 2 million tokens. According to the authors, there are no fundamental limitations for this value to increase further, as the computational complexity of RMT grows linearly with the number of tokens. The accuracy of the pre-trained BERT model augmented with RMT on three tasks vs the number of tokens in the input sequence. The gray numbers indicate the GPU memory consumption, and the vertical lines represent the length limits in SOTA models (as of the end of 2023) The research was published in the proceedings of the AAAI-24 conference, additional details are provided in the preprint, and the code is available on GitHub. submitted by /u/AIRI_Institute [link] [comments]

Paper: https://arxiv.org/abs/2404.11912 Code: https://github.com/Infini-AI-Lab/TriForce Project page: https://infini-ai-lab.github.io/TriForce/ Abstract: With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the sequence length. Due to the auto-regressive nature of LLMs, the entire KV cache will be loaded for every generated token, resulting in low utilization of computational cores and high latency. While various compression methods for KV cache have been proposed to alleviate this issue, they suffer from degradation in generation quality. We introduce TriForce, a hierarchical speculative decoding system that is scalable to long sequence generation. This approach leverages the original model weights and dynamic sparse KV cache via retrieval as a draft model, which serves as an intermediate layer in the hierarchy and is further speculated by a smaller model to reduce its drafting latency. TriForce not only facilitates impressive speedups for Llama2-7B-128K, achieving up to 2.31× on an A100 GPU but also showcases scalability in handling even longer contexts. For the offloading setting on two RTX 4090 GPUs, TriForce achieves 0.108s/token—only half as slow as the auto-regressive baseline on an A100, which attains 7.78× on our optimized offloading system. Additionally, TriForce performs 4.86× than DeepSpeed-Zero-Inference on a single RTX 4090 GPU. TriForce's robustness is highlighted by its consistently outstanding performance across various temperatures. The code is available at this https URL. submitted by /u/SeawaterFlows [link] [comments]

Paper: https://arxiv.org/abs/2404.11018 Abstract: Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousands of examples -- the many-shot regime. Going from few-shot to many-shot, we observe significant performance gains across a wide variety of generative and discriminative tasks. While promising, many-shot ICL can be bottlenecked by the available amount of human-generated examples. To mitigate this limitation, we explore two new settings: Reinforced and Unsupervised ICL. Reinforced ICL uses model-generated chain-of-thought rationales in place of human examples. Unsupervised ICL removes rationales from the prompt altogether, and prompts the model only with domain-specific questions. We find that both Reinforced and Unsupervised ICL can be quite effective in the many-shot regime, particularly on complex reasoning tasks. Finally, we demonstrate that, unlike few-shot learning, many-shot learning is effective at overriding pretraining biases and can learn high-dimensional functions with numerical inputs. Our analysis also reveals the limitations of next-token prediction loss as an indicator of downstream ICL performance. submitted by /u/SeawaterFlows [link] [comments]

I'm trying to create a web app that recognizes logos of brands in images. I've tried using Microsoft Azure Computer Vision/Custom Vision API but the results are not satisfactory. I have read about Yolo and Yolo world. If you've ever used them in your projects, can you help me see if zero shot logo detection can be achieved by this? submitted by /u/CommercialFragrant [link] [comments]

Looking for brutally honest opinions. Is the reality different for data engineers as I find the supply demand makes DE attractive currently ? submitted by /u/Emergency-Director53 [link] [comments]

In the dpo paper, the authors recommended to do SFT prior to doing DPO to prevent distribution shift and also demonstrated the discrepancy in performance for non-SFT and SFT in the new paper: https://arxiv.org/pdf/2404.12358.pdf However, i am slightly unsure about whats the rule for curating the preference dataset using a SFT-ed model. Does it mean that before doing DPO, the ref model HAS to be SFTed on the same prompts (x) of the preference dataset/similar distribution dataset? OR the preference dataset has to be curated from the ref model? The latter would mean that you could do SFT on any dataset so long as the pref dataset is curated using the SFT-ed model and not using any available pref dataset you find online, which most likely is curated using some unknown policy. While the former is saying that the ref policy has to be SFTed on the same distribution of the pref dataset (ie similar prompt types), meaning this is just an additional SFT step on the pref dataset's chosen response as compared to the previous case. What are your thoughts on this? submitted by /u/nohodlnodough [link] [comments]

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Almost no job posting for Data Scientist or ML Engineer needs GNNs. Is it because it's computationally expensive - both time and space? Or is it because preprocessing data to a graph format is not always intuitive? Or GNN awareness is still low outside of academia? submitted by /u/Snoo_72181 [link] [comments]

TL;DR: Tesla uses lightweight "trigger classifiers" to detect rare scenarios when their ML model underperforms. Relevant data is uploaded to a server to improve the model, which is then trained again to cover different failure modes. How Tesla Continuously and Automatically Improves Autopilot and Full Self-Driving Capability On 5M+ Cars. A visual guide: How Tesla sets up their iterative ML pipeline P.S.: I spent several hours researching and preparing a visual deep dive of Tesla’s data engine as pioneered by Andrej Karpathy. The post lays out the iterative recipe of how Tesla improves it's fully self-driving and Autopilot capabilities. https://preview.redd.it/qxmjeavmjvvc1.jpg?width=1456&format=pjpg&auto=webp&s=94cb35f71f7e57b6bcc6e0bf9f1d5f05b5c7f086 https://preview.redd.it/htz4p8vmjvvc1.jpg?width=1456&format=pjpg&auto=webp&s=a722604b59d2c6fbb8f7e605ad496bede05a238e submitted by /u/ml_a_day [link] [comments]

Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]

When I first started my ML journey about 4 years ago the most basic intro level recommendation algorithms I learned were about collaborative filtering and content based filtering. I want to know more about what the current state of recommender systems is. How has it changed? What methods are people trying to include in search of better recommendations? Has there been any mention of including causality in recommendation systems? This latter seems like the most “up to date” advancement, but I have yet to find an overview. submitted by /u/Direct-Touch469 [link] [comments]

Amazon Personalize is excited to announce automatic training for solutions. Solution training is fundamental to maintain the effectiveness of a model and make sure recommendations align with users’ evolving behaviors and preferences. As data patterns and trends change over time, retraining the solution with the latest relevant data enables the model to learn and adapt,

We are excited to announce a new version of the Amazon SageMaker Operators for Kubernetes using the AWS Controllers for Kubernetes (ACK). ACK is a framework for building Kubernetes custom controllers, where each controller communicates with an AWS service API. These controllers allow Kubernetes users to provision AWS resources like buckets, databases, or message queues

In Part 1 of this series, we presented a solution that used the Amazon Titan Multimodal Embeddings model to convert individual slides from a slide deck into embeddings. We stored the embeddings in a vector database and then used the Large Language-and-Vision Assistant (LLaVA 1.5-7b) model to generate text responses to user questions based on

This is a guest post co-written with the leadership team of Iambic Therapeutics. Iambic Therapeutics is a drug discovery startup with a mission to create innovative AI-driven technologies to bring better medicines to cancer patients, faster. Our advanced generative and predictive artificial intelligence (AI) tools enable us to search the vast space of possible drug

As you navigate the complexities of cloud migration, the need for a structured, secure, and compliant environment is paramount. AWS Landing Zone addresses this need by offering a standardized approach to deploying AWS resources. This makes sure your cloud foundation is built according to AWS best practices from the start. With AWS Landing Zone, you eliminate the guesswork in security configurations, resource provisioning, and account management. It’s particularly beneficial for organizations looking to scale without compromising on governance or control, providing a clear path to a robust and efficient cloud setup. In this post, we show you how to generate customized, compliant IaC scripts for AWS Landing Zone using Amazon Bedrock.

You’ve likely experienced the challenge of taking notes during a meeting while trying to pay attention to the conversation. You’ve probably also experienced the need to quickly fact-check something that’s been said, or look up information to answer a question that’s just been asked in the call. Or maybe you have a team member that always joins meetings late, and expects you to send them a quick summary over chat to catch them up. Then there are the times that others are talking in a language that’s not your first language, and you’d love to have a live translation of what people are saying to make sure you understand correctly. And after the call is over, you usually want to capture a summary for your records, or to send to the participants, with a list of all the action items, owners, and due dates. All of this, and more, is now possible with our newest sample solution, Live Meeting Assistant (LMA).

Today, we are excited to announce that Meta Llama 3 foundation models are available through Amazon SageMaker JumpStart to deploy and run inference. The Llama 3 models are a collection of pre-trained and fine-tuned generative text models. In this post, we walk through how to discover and deploy Llama 3 models via SageMaker JumpStart. What is

We are excited to announce that Slack, a Salesforce company, has collaborated with Amazon SageMaker JumpStart to power Slack AI’s initial search and summarization features and provide safeguards for Slack to use large language models (LLMs) more securely. Slack worked with SageMaker JumpStart to host industry-leading third-party LLMs so that data is not shared with the infrastructure owned by third party model providers. This keeps customer data in Slack at all times and upholds the same security practices and compliance standards that customers expect from Slack itself.

In asset management, portfolio managers need to closely monitor companies in their investment universe to identify risks and opportunities, and guide investment decisions. Tracking direct events like earnings reports or credit downgrades is straightforward—you can set up alerts to notify managers of news containing company names. However, detecting second and third-order impacts arising from events

This post walks you through the Open Source Observability pattern for AWS Inferentia, which shows you how to monitor the performance of ML chips, used in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster, with data plane nodes based on Amazon Elastic Compute Cloud (Amazon EC2) instances of type Inf1 and Inf2.

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate

In this post, we explore the performance benefits of Amazon SageMaker (including SMP and SMDDP), and how you can use the library to train large models efficiently on SageMaker. We demonstrate the performance of SageMaker with benchmarks on ml.p4d.24xlarge clusters up to 128 instances, and FSDP mixed precision with bfloat16 for the Llama 2 model.

Amazon Lex is a fully managed artificial intelligence (AI) service with advanced natural language models to design, build, test, and deploy conversational interfaces in applications. It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language. Managing your

Generative artificial intelligence (AI) is transforming the customer experience in industries across the globe. Customers are building generative AI applications using large language models (LLMs) and other foundation models (FMs), which enhance customer experiences, transform operations, improve employee productivity, and create new revenue channels. The biggest concern we hear from customers as they explore the advantages of generative AI is how to protect their highly sensitive data and investments. At AWS, our top priority is safeguarding the security and confidentiality of our customers' workloads. We think about security across the three layers of our generative AI stack ...

Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and extract information remains expensive, error prone, and difficult to scale. Advances in generative artificial intelligence (AI) have given rise to intelligent document processing (IDP) solutions that can automate the document classification,

AWS was delighted to present to and connect with over 18,000 in-person and 267,000 virtual attendees at NVIDIA GTC, a global artificial intelligence (AI) conference that took place March 2024 in San Jose, California, returning to a hybrid, in-person experience for the first time since 2019. AWS has had a long-standing collaboration with NVIDIA for

This blog post is co-written with Caroline Chung from Veoneer. Veoneer is a global automotive electronics company and a world leader in automotive electronic safety systems. They offer best-in-class restraint control systems and have delivered over 1 billion electronic control units and crash sensors to car manufacturers globally. The company continues to build on a

With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for Retrieval Augmented Generation (RAG). Access to additional data helps the model generate more relevant, context-specific, and accurate responses without retraining the FMs. In this post, we discuss two new features of Knowledge Bases for