Hey All, I think many of us can agree that defining the hardware we want to use right next to the piece of code we are running is objectively a much better developer experience. I have always loved the idea of lowering the barrier when it comes to running code in the cloud. As more cloud abstractions hit the market, I was honestly really surprised by the lack of adoption. There aren't any unicorns (I don't think any actually) in this space yet, just series A businesses. After speaking with a handful of Data Scientists, Machine Learning Engineers, and DevOps Engineers, it started to dawn on me that risk aversion is causing most of the friction. Using a fully managed service can definitely have some upsides, and in many cases, I prefer using them, but convincing your boss to pipe petabytes of data to another company's cloud and incur 3-5x compute costs probably isn't going to sit well. There are also some open source alternatives but they are intentionally difficult to configure so you pay for their premium offerings that reduce config setup. Would love to hear everyone's thoughts, especially those who work at lean startups and global 5,000 companies. submitted by /u/Ok_Post_149 [link] [comments]

I am curious to know the job opportunities in Banking & Insurance for someone pursuing PhD in Statistics given the current market conditions. submitted by /u/SpiritualCellist4303 [link] [comments]

Title. Is there a way to determine the degree of sphericity or hyperbolicity of the embeddings a feature extractor learns for a set of examples it has been trained on / will be tested on? I am new to geometry in deep learning. It would be amazing if anyone could also point me to a paper or a book to get started on this. Thanks in advance. submitted by /u/Mad_Scientist2027 [link] [comments]

Paper: https://arxiv.org/abs/2104.10751 Package: https://github.com/sametcopur/ruleopt Documentation: https://ruleopt.readthedocs.io/ RuleOpt is an optimization-based rule learning algorithm designed for classification problems. Focusing on scalability and interpretability, RuleOpt utilizes linear programming for rule generation and extraction. The Python library ruleopt is capable of extracting rules from ensemble models, and it also implements a novel rule generation scheme. The library ensures compatibility with existing machine learning pipelines, and it is especially efficient for tackling large-scale problems. Here are a few highlights of ruleopt: Efficient Rule Generation and Extraction: Leverages linear programming for scalable rule generation (stand-alone machine learning method) and rule extraction from trained random forest and boosting models. Interpretability: Prioritizes model transparency by assigning costs to rules in order to achieve a desirable balance with accuracy. Integration with Machine Learning Libraries: Facilitates smooth integration with well-known Python libraries scikit-learn, LightGBM, and XGBoost, and existing machine learning pipelines. Extensive Solver Support: Supports a wide array of solvers, including Gurobi, CPLEX and OR-Tools. submitted by /u/zedeleyici3401 [link] [comments]

I've been using LSTM models for time series forecasting and have noticed they perform well for predicting the immediate next step. However, when attempting multi-step predictions to forecast one week ahead (168 periods, with hourly data), the performance drops significantly. Currently, I'm using a recursive approach: feeding back the prediction as the next input (closed loop). This method isn't yielding good results, although open loop predictions are much more accurate. Is there a better technique for enhancing LSTM's multi-step prediction accuracy? Are LSTMs not useful for doing multi step forecasting? Any links or resources to articles explain multi step forecasting with LSTMs would be appreciated. https://preview.redd.it/30y3m16gr3vc1.png?width=833&format=png&auto=webp&s=6d6b29e05b105b50d2689127ea6881d1ec667903 https://preview.redd.it/a971j16gr3vc1.png?width=833&format=png&auto=webp&s=fec277d9343c5f702247a6135dbb630358c14cca submitted by /u/StressAccomplished26 [link] [comments]

Paper: https://arxiv.org/abs/2404.07738 Abstract: Scientific Research, vital for improving human life, is hindered by its inherent complexity, slow pace, and the need for specialized experts. To enhance its productivity, we propose a ResearchAgent, a large language model-powered research idea writing agent, which automatically generates problems, methods, and experiment designs while iteratively refining them based on scientific literature. Specifically, starting with a core paper as the primary focus to generate ideas, our ResearchAgent is augmented not only with relevant publications through connecting information over an academic graph but also entities retrieved from an entity-centric knowledge store based on their underlying concepts, mined and shared across numerous papers. In addition, mirroring the human approach to iteratively improving ideas with peer discussions, we leverage multiple ReviewingAgents that provide reviews and feedback iteratively. Further, they are instantiated with human preference-aligned large language models whose criteria for evaluation are derived from actual human judgments. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines, showcasing its effectiveness in generating novel, clear, and valid research ideas based on human and model-based evaluation results. submitted by /u/SeawaterFlows [link] [comments]

Paper: https://arxiv.org/abs/2404.09967 Code: https://github.com/HL-hanlin/Ctrl-Adapter Models: https://huggingface.co/hanlincs/Ctrl-Adapter Project page: https://ctrl-adapter.github.io/ Abstract: ControlNets are widely used for adding spatial control in image generation with different conditions, such as depth maps, canny edges, and human poses. However, there are several challenges when leveraging the pretrained image ControlNets for controlled video generation. First, pretrained ControlNet cannot be directly plugged into new backbone models due to the mismatch of feature spaces, and the cost of training ControlNets for new backbones is a big burden. Second, ControlNet features for different frames might not effectively handle the temporal consistency. To address these challenges, we introduce Ctrl-Adapter, an efficient and versatile framework that adds diverse controls to any image/video diffusion models, by adapting pretrained ControlNets (and improving temporal alignment for videos). Ctrl-Adapter provides diverse capabilities including image control, video control, video control with sparse frames, multi-condition control, compatibility with different backbones, adaptation to unseen control conditions, and video editing. In Ctrl-Adapter, we train adapter layers that fuse pretrained ControlNet features to different image/video diffusion models, while keeping the parameters of the ControlNets and the diffusion models frozen. Ctrl-Adapter consists of temporal and spatial modules so that it can effectively handle the temporal consistency of videos. We also propose latent skipping and inverse timestep sampling for robust adaptation and sparse control. Moreover, Ctrl-Adapter enables control from multiple conditions by simply taking the (weighted) average of ControlNet outputs. With diverse image/video diffusion backbones (SDXL, Hotshot-XL, I2VGen-XL, and SVD), Ctrl-Adapter matches ControlNet for image control and outperforms all baselines for video control (achieving the SOTA accuracy on the DAVIS 2017 dataset) with significantly lower computational costs (less than 10 GPU hours). submitted by /u/SeawaterFlows [link] [comments]

Hello! I am a researcher in computational neuroscience, looking to apply some contemporary machine learning techniques to fMRI timeseries data. I have a collection of highly dimensional 4D fMRI timeseries data collected while subjects were observing naturalistic images from COCO at regular intervals. We currently have decoding models that take preprocessed "snapshots" of this timeseries data flattened into an activation pattern that is aggregated over the short period the image was being observed, and use some machine learning models to decode and reconstruct the image content from the brain. (See some of my recent work). I am curious what sort of machine learning techniques exist that might be able to address the time-series data itself, without having to collapse the timeseries to a single snapshot to perform our decoding process. What I am envisioning is a model (perhaps a transformer) that can take as input a highly dimensional multichannel timeseries and output a flattened latent representation (say, a CLIP vector) corresponding to an image stimulus, or even a series of latent vectors separated by a known regular interval (as we have in our data for the different image presentations). To my knowledge most of the work in machine learning with time series data is in forecasting, but what I want is a static (or potentially repetitive) output. My hope is that the more detailed timeseries data will have additional signal that will boost decoding performance for fMRI vision decoding. Is there any existing work in the field of ML that has tackled a similar problem? submitted by /u/reesespike [link] [comments]

I have built a model for detecting outliers in a data series using ML.NET. I read from the dev forums that ML.NET using DirectML can support the new NPUs built into the new Core Ultra processors from Intel. I have not been able to find evidence that this is true for AutoML from the Microsoft team. Does anyone have experience using AutoML with DirectML backend? submitted by /u/tradingnumbers [link] [comments]

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.

I looked up in so many places but couldn't find an answer. What happens if we switch Q and K to be from the encoder and decoder respectively? Would it make any difference? submitted by /u/shuvamg007 [link] [comments]

Hello everyone! I'm excited to discuss about Large Vision Language Model (LVLM). Since we're probably the biggest community into LLMs, I thought this channel would be the perfect place to start this conversation. Also, there isn't much out there on combining vision and language embeddings. A little background on LVLMs: They typically consist of a vision encoder for images, a regular tokenizer for text, a projection layer like an MLP to align vision features with text embedding spaces, and finally, merging both image and text embeddings for sending into the LLM model. The input includes both text and images, while the output is text, making it a multimodal LLM. Check out this diagram from the LLaVA paper for a visual breakdown: https://preview.redd.it/l222askgu1vc1.png?width=1607&format=png&auto=webp&s=ef011e16301c22b4751d8d0a8f3698f70e3ffd26 Starting with a vision encoder like CLIP ViT, the model learns visual information from images, then uses an MLP to project this onto the LLM's embedding space. The paper calls this feature alignment. I'm curious about how vision embeddings interact with text embeddings, so I experimented by visualizing them in 3D with PCA. For instance, take the llava-7B model—it uses the llama-7B backend with a 32k vocabulary size and 4096 dimensions, making the embedding size: [32000,4096]. I used a simple prompt, "Explain this image to me," with a picture of a cat to see how the embeddings appear in our space. https://preview.redd.it/032oy0ynu1vc1.png?width=662&format=png&auto=webp&s=d037bbecc976392e159a1c1bde775ef1e148488d Adding visual tokens changes the dynamics. Each image transforms into 576 vision tokens of shape [576,4096]. Check out how the plot adjusts when these tokens are included: https://preview.redd.it/9c3cu7ksu1vc1.png?width=660&format=png&auto=webp&s=c0aab6782fc309eba09ec660759bfaf48582dc14 The entire text embedding seems smaller, represented by tiny blue dots containing the entire llama-7B vocabulary. To zoom in further, I highlighted only the visual tokens near the embedding (meaning higher cosine similarity). Here’s how they cluster together: https://preview.redd.it/vdeacylwu1vc1.png?width=566&format=png&auto=webp&s=42441b4fd515cee916b40243429b4aa6820b998c So what do I think? First, we aren't directly converting visual tokens into text. A recent Google paper tried and found it wasn't the best approach. It seems that visual reasoning hovers close to text embedding spaces, likely because images are denser in information, requiring more tokens to represent visual concepts. Secondly, this setup seems right for now. Visual tokens, in context with text tokens, add image-derived context to the LLM, enabling it to 'see' an image. Lastly, even though llava is performing well on some benchmarks in visual reasoning, it might not be the most efficient at image representation yet. Some recent studies talked about it's sparse attention phenomenon, especially with visual tokens in LVLMs. We are just lucky because the attention algorithm attends to only meaningful visual tokens and ignores the noises. What do you think? Thanks for reading. 🙂 submitted by /u/E-fazz [link] [comments]

Can anyone recommend any good resources on modern time series forecasting with machine learning? I found one book on time series forecasting on Amazon with great reviews called Time Series Forecasting in Python. Having said that, a lot of machine learning books and resources seem to gloss over time series. What are some good resources (either entire books, or chapters in books) that cover time series? submitted by /u/secret_fyre [link] [comments]

Hi I am aware that a lot of transformer encoder variations exist (BERT, DistilBERT, Deberta, Roberta ...). However I am not interested in the best ones (that should probably be Deberta V3) but rather the ones that can quickly have decent results even with very few example examples (like ~50,100 sentences each containing maybe 1, 2 or 3 entities). I have done a few experiments in english, and to my surprise it seems that the one that perform best with as few data as possible is the original english BERT model (google-bert/bert-base-uncased on HF), and not one of the more recent variations. I have also done other experiments in french, and the multilingual BERT also quickly get decent results faster than models specially trained on french data (e.g CamemBERT). The models I've compared include : bert, bert multilingual, distilbert, distilbert multilingual, roberta, xlm-roberta, camembert, camemberta, distilroberta, debertav3, debertav3 multilingual What are your thought about this ? Is it something surprising or unusual ? Any advice ? submitted by /u/LelouchZer12 [link] [comments]

Noob question about word embeddings - As far as I understand so far - Contextualized word embeddings generated by BERT and other LLM type models use the attention mechanism and take into account the context of the word. So the same word in different sentences can have different vectors. This ^ is opposed to the older approach of models like word2vec - embeddings generated by word2vec are not contexual. However, looking closely at the CBOW and skip-gram models. it seems that they too try to predict the central word based on the surrounding (context) words. So the embeddings generated by word2vec can also be contextual. So they're both contexutal? What am I missing? submitted by /u/datashri [link] [comments]

By modern I mean any new innovation in the field of Speaker Verification. I was researching more about ML in the field of Audio - Speech in particular and I notice there are so many things going on right now with LLM being integrated into almost everything. So I was curious to know if there is any new innovation in the field of Speaker Recognition. Some of the cool libraries I came across were - pyannote.audio, speechbrain, Nvidia NeMo which provide the framework and pre-trained models for the task of Speaker Verification. Thanks in advance! ​ submitted by /u/Puzzleheaded_Bee5489 [link] [comments]

I have been doing some fine tuning work, and I am adjusting the weight decay and learning rate of my transformer models. My base model is BERT, and the fine tune data set is quite small. The issue I had was when I set incorrect hyperparameters, the model do not do anything. For example, if the optimal learning rate is 5e-3, but I am tuning it using 1e-2, 1e-3, 1e-4. Then the F measures are all 0.0. I understand the hyperparameter affects the results a lot. But I didn't expect it to be learn or not learn at all. I wonder if it is normal. cause 5e-3 and 1e-3 is not that much difference? submitted by /u/FFFFFQQQQ [link] [comments]

This is the first year (if I remember correclty) that MM shifts from CMT to Openreview. As an author I've been sensing something wrong since I created my submission, i.e. desk rejection even before abstract ddl, inconsistency about whether to include submission number within the paper, etc. Now I've heard a lot from social media that many authors without many/any publications (yes including me) have been nominated as reviewers due to their lack of reviewers for the submission volume. I'm very concerned about the quality of the reviews and the submission in MM2024 this year. submitted by /u/INeedPapers_TTT [link] [comments]

I am currently working on a fed batch process and I need to know if time series can be achieved with good accuracy. The idea is that there is a set of differential equations which create the data for me. After this data, a model is created with high accuracy. The question is, is it possible to achieve a time series prediction by giving the model a complete different set of initial conditions? My job is to have this model predict in completely different initial conditions so there is no need for real life testing on the batch process but just computational. I was looking into Neural ODE, UDE etc. in order for the model to understand the dynamics but I am also not sure if other methods of time series would work. (The data has no periodicity, correlated between each other etc.) What do you think would be the best approach since I am constantly trying different methods but none give accurate results? submitted by /u/Bitter__Physics [link] [comments]

I'm learning meths behind diffusion right now (DDPM, Score-based, and other approaches). I'm wondering how exactly did researchers come up with the idea? Does inventing new approaches go something like this? 1. We want to make better image generator. 2. Oh, the data will never be enough... 3. Let's multiply data - by adding some noise corruption 4. This this works well, what if we make a denoising network? 5. What if we make network that makes an image from pure noise? 6. That doesn't work, what if we did smaller denoising steps? 7. This works! Now, let's create some theory on why it works. 8. Write the paper Or something like this? 1. We want to make better image generator. 2. We know "nonequilibrium thermodynamics" really well and want to try applying it somehow 3. We somehow come up with an algorithm that relies on math from that theory 4. It works! 5. We write the paper. Which comes first usually? Math or Algorithm? submitted by /u/Deep-Station-1746 [link] [comments]

Saw this informative video on the server company Gigabyte's website (https://youtu.be/2Q7S-CbnAAY?si=DJtU2mQ_ZKRZ83Nf), the short version is that server brands are now shipping complete clusters of servers to data centers instead of individual machines. In the example shown here, it's 8 racks (plus one extra for management and networking), with 4 servers of the same model in each rack, and with 4 super-advanced GPUs of the same model in each server. To do the math for you, that's 32 servers or 256 GPU accelerators per cluster. Take note that all the servers and GPUs have to be the same model because they are connected in a way that they basically operate as one individual machine. The reason this is very likely to be the standard building block in all AI data centers is that the way we are training AI off of large datasets right now, the parameters are numbering in the billions, even the trillions. This is especially true for LLMs that brought us ChatGPT and its ilk. The only way to handle these trillions of parameters with any efficiency is through parallel computing on a scale we've never seen before. Hence this bold new concept of connecting hundreds, even thousands of servers together so they are basically one giant server that's loaded thousands of GPUs by Nvidia or other brands. Truly fascinating stuff and I've not seen anything else on this scale that's currently being proposed for the future of AI computing. Here's the website of the cluster introduced in the video: https://www.gigabyte.com/Industry-Solutions/giga-pod-as-a-service?lan=en submitted by /u/Low_Complaint2254 [link] [comments]

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I’m looking to play around with ML models related to speech, particularly singing/vocals. Are there any papers/resources anyone can recommend to get started on creating my own model or open-source models that can be expanded on? The trouble with the models I see online is that they can only change voices given an existing vocals or aren’t open-source, but I’d like to see where it can take text and create vocals in different enunciations. Thanks! submitted by /u/sometimesnotcool [link] [comments]

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

Since it seems like almost every dataset can be converted to a graph : Tabular - Nodes as rows with no edges between them Text and Audio - Nodes as words with directed edges between adjacent words Time Series - Same as 2 Image - Nodes as pixels with undirected edges between adjacent pixels (including diagonal) Even if GNNs can work on all types of data, I think it may be time and space intensive to covert them into graphs, especially in case of Images. At the same time, GNNs can make some Tabular data based ML models even more accurate - for e.g. if we have a Tabular dataset on Apartment Pricing, we can add edges between apartments in the same neighborhood so that all their prices are dependent on one another, and this models real-life phenomenon of how apartments in the same neighborhood have codependent pricing based on state of the neighborhood (for e.g. if crimes increase in the neighborhood, all apartments have their prices go down) submitted by /u/Snoo_72181 [link] [comments]

I created an app for software engineers called Snipman.io >>> https://snipman.io It is a self hosted code snippet management app (currently free to download on Mac and Windows) that basically lets you store snippets by snippet types. I primarily created it because I found myself creating a lot of text files for small code snippets for different programming languages, frameworks, tools, cloud, devOps and technologies for e.g Python, PyTorch, AWS, GCP, Terraform, Kubernetes, Docker etc. This not only resulted in a lot of clutter but also a pain when it came to searching and locating the correct snippet. My goal was to create something that would allow all the commands, configs and snippets to be stored in a central repo locally and then have the ability to search them quickly. I believe my app helps achieve all of that through an elegant and simple to use GUI based tool. I hope all the community members here find it useful! ​ Pytorch snippet example in snipman.io ​ submitted by /u/dev_user1091 [link] [comments]

​ https://preview.redd.it/5vqtawmqavuc1.png?width=1216&format=png&auto=webp&s=5a111c704b7e0b017dcd8b4bfb0e974c3e6069dc https://huggingface.co/blog/idefics2 submitted by /u/VictorSanh [link] [comments]

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 ...

submitted by /u/Appropriate_Ant_4629 [link] [comments]

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

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data using a fully managed Retrieval Augmented Generation (RAG) model. For RAG-based applications, the accuracy of the generated responses from FMs

Unlocking accurate and insightful answers from vast amounts of text is an exciting capability enabled by large language models (LLMs). When building LLM applications, it is often necessary to connect and query external data sources to provide relevant context to the model. One popular approach is using Retrieval Augmented Generation (RAG) to create Q&A systems

With the widespread adoption of generative artificial intelligence (AI) solutions, organizations are trying to use these technologies to make their teams more productive. One exciting use case is enabling natural language interactions with relational databases. Rather than writing complex SQL queries, you can describe in plain language what data you want to retrieve or manipulate.

In January 2024, Amazon SageMaker launched a new version (0.26.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs). This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion

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]

Amazon Rekognition makes it easy to add image and video analysis to your applications. It’s based on the same proven, highly scalable, deep learning technology developed by Amazon’s computer vision scientists to analyze billions of images and videos daily. It requires no machine learning (ML) expertise to use and we’re continually adding new computer vision

This is a guest post co-authored by Shravan Kumar and Avirat S from Gramener. Gramener, a Straive company, contributes to sustainable development by focusing on agriculture, forestry, water management, and renewable energy. By providing authorities with the tools and insights they need to make informed decisions about environmental and social impact, Gramener is playing a

With a multitude of articles, videos, audio recordings, and other media created daily across news media companies, readers of all types—individual consumers, corporate subscribers, and more—often find it difficult to find news content that is most relevant to them. Delivering personalized news and experiences to readers can help solve this problem, and create more engaging

This is a guest post co-written with Tamir Rubinsky and Aviad Aranias from Nielsen Sports. Nielsen Sports shapes the world’s media and content as a global leader in audience insights, data, and analytics. Through our understanding of people and their behaviors across all channels and platforms, we empower our clients with independent and actionable intelligence

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. SageMaker Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. Amazon SageMaker Canvas is a powerful

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model, available in Amazon Bedrock, with Amazon OpenSearch Serverless.