Alternatives to Embeddinghub

Qdrant is a vector database and similarity engine. It is an API service that allows you to search for the closest high-dimensional vectors. Qdrant allows embeddings and neural network encoders to be transformed into full-fledged apps for matching, searching, recommending, etc. This specification provides the OpenAPI version 3 specification to create a client library for almost any programming language. You can also use a ready-made client for Python, or other programming languages that has additional functionality. For Approximate Nearest Neighbor Search, you can make a custom modification to the HNSW algorithm. Search at a State of the Art speed and use search filters to maximize results. Additional payload can be associated with vectors. Allows you to store payload and filter results based upon payload values.

Artificial intelligence long-term memory The Pinecone vector database makes building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements. Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.

A vector database designed for scalable similarity searches. Open-source, highly scalable and lightning fast. Massive embedding vectors created by deep neural networks or other machine learning (ML), can be stored, indexed, and managed. Milvus vector database makes it easy to create large-scale similarity search services in under a minute. For a variety languages, there are simple and intuitive SDKs. Milvus is highly efficient on hardware and offers advanced indexing algorithms that provide a 10x speed boost in retrieval speed. Milvus vector database is used in a variety a use cases by more than a thousand enterprises. Milvus is extremely resilient and reliable due to its isolation of individual components. Milvus' distributed and high-throughput nature makes it an ideal choice for large-scale vector data. Milvus vector database uses a systemic approach for cloud-nativity that separates compute and storage.

Marqo is a complete vector search engine. It's more than just a database. A single API handles vector generation, storage and retrieval. No need to embed your own embeddings. Marqo can accelerate your development cycle. In just a few lines, you can index documents and start searching. Create multimodal indexes, and search images and text combinations with ease. You can choose from a variety of open-source models or create your own. Create complex and interesting queries with ease. Marqo allows you to compose queries that include multiple weighted components. Marqo includes input pre-processing and machine learning inference as well as storage. Marqo can be run as a Docker on your laptop, or scaled up to dozens GPU inference nodes. Marqo is scalable to provide low latency searches on multi-terabyte indices. Marqo allows you to configure deep-learning models such as CLIP for semantic meaning extraction from images.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

Use user feedback and semantic relevance to reliably retrieve optimal document chunks for your retrieval-augmented generation system. In your search system, combine semantic relevance with document freshness because recent results are more accurate. Create a personalized ecommerce feed in real-time using user vectors based on the SKU embeddings that were viewed by the user. A vector index in your warehouse can be used to discover behavioral clusters among your customers. Use spaces to build your indices, and run queries all within a Python Notebook.

Chroma is an AI-native, open-source embedding system. Chroma provides all the tools needed to embeddings. Chroma is creating the database that learns. You can pick up an issue, create PRs, or join our Discord to let the community know your ideas.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Users build recommendation applications on Vespa, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

Weaviate is an open source vector database. It allows you to store vector embeddings and data objects from your favorite ML models, and scale seamlessly into billions upon billions of data objects. You can index billions upon billions of data objects, whether you use the vectorization module or your own vectors. Combining multiple search methods, such as vector search and keyword-based search, can create state-of-the art search experiences. To improve your search results, pipe them through LLM models such as GPT-3 to create next generation search experiences. Weaviate's next generation vector database can be used to power many innovative apps. You can perform a lightning-fast, pure vector similarity search on raw vectors and data objects. Combining keyword-based and vector search techniques will yield state-of the-art results. You can combine any generative model with your data to do Q&A, for example, over your dataset.

Faiss is a library that allows for efficient similarity searches and clustering dense vectors. It has algorithms that can search for vectors of any size. It also includes supporting code for parameter tuning and evaluation. Faiss is written entirely in C++ and includes wrappers for Python. The GPU is home to some of the most powerful algorithms. It was developed by Facebook AI Research.

We've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook.

Vald is a distributed, fast, dense and highly scalable vector search engine that approximates nearest neighbors. Vald was designed and implemented using the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT for searching neighbors. Vald supports automatic vector indexing, index backup, horizontal scaling, which allows you to search from billions upon billions of feature vector data. Vald is simple to use, rich in features, and highly customizable. Usually, the graph must be locked during indexing. This can cause stop-the world. Vald uses distributed index graphs so that it continues to work while indexing. Vald has its own highly customizable Ingress/Egress filter. This can be configured to work with the gRPC interface. Horizontal scaling is available on memory and cpu according to your needs. Vald supports disaster recovery by enabling auto backup using Persistent Volume or Object Storage.

Semantee, a managed database that is easy to configure and optimized for semantic searches, is hassle-free. It is available as a set REST APIs that can be easily integrated into any application in minutes. It offers multilingual semantic searching for applications of any size, both on-premise and in the cloud. The product is significantly cheaper and more transparent than most providers, and is optimized for large-scale applications. Semantee also offers an abstraction layer over an e-shop's product catalog, enabling the store to utilize semantic search instantly without having to re-configure its database.

Atlas integrates with your workflow by organizing text, embedding datasets and creating interactive maps that can be explored in a web browser. To understand your data, you don't need to scroll through Excel files or log Dataframes. Atlas automatically analyzes, organizes, and summarizes your documents, surfacing patterns and trends. Atlas' pre-organized data interface makes it easy to quickly identify and remove any data that could be harmful to your AI projects. You can label and tag your data, while cleaning it up with instant sync to your Jupyter notebook. Although vector databases are powerful, they can be difficult to interpret. Atlas stores, visualizes, and allows you to search through all your vectors within the same API.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

Postgres: Open-source vector similarity search Supports exact and approximate closest neighbor search for L2 distances, inner product and cosine distances.

Astra DB from DataStax is a real-time vector database as a service for developers that need to get accurate Generative AI applications into production, fast. Astra DB gives you a set of elegant APIs supporting multiple languages and standards, powerful data pipelines and complete ecosystem integrations. Astra DB enables you to quickly build Gen AI applications on your real-time data for more accurate AI that you can deploy in production. Built on Apache Cassandra, Astra DB is the only vector database that can make vector updates immediately available to applications and scale to the largest real-time data and streaming workloads, securely on any cloud. Astra DB offers unprecedented serverless, pay as you go pricing and the flexibility of multi-cloud and open-source. You can store up to 80GB and/or perform 20 million operations per month. Securely connect to VPC peering and private links. Manage your encryption keys with your own key management. SAML SSO secure account accessibility. You can deploy on Amazon, Google Cloud, or Microsoft Azure while still compatible with open-source Apache Cassandra.

KDB.AI, a powerful knowledge based vector database, is a powerful search engine and knowledge-based vector data base that allows developers to create scalable, reliable, and real-time AI applications. It provides advanced search, recommendation, and personalization. Vector databases are the next generation of data management, designed for applications such as generative AI, IoT or time series. Here's what makes them unique, how they work and the new applications they're designed to serve.

MyScale is a cutting-edge AI database that combines vector search with SQL analytics, offering a seamless, fully managed, and high-performance solution. Key features of MyScale include: - Enhanced data capacity and performance: Each standard MyScale pod supports 5 million 768-dimensional data points with exceptional accuracy, delivering over 150 QPS. - Swift data ingestion: Ingest up to 5 million data points in under 30 minutes, minimizing wait times and enabling faster serving of your vector data. - Flexible index support: MyScale allows you to create multiple tables, each with its own unique vector indexes, empowering you to efficiently manage heterogeneous vector data within a single MyScale cluster. - Seamless data import and backup: Effortlessly import and export data from and to S3 or other compatible storage systems, ensuring smooth data management and backup processes. With MyScale, you can harness the power of advanced AI database capabilities for efficient and effective data analysis.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

NVIDIA Modulus, a neural network framework, combines the power of Physics in the form of governing partial differential equations (PDEs), with data to create high-fidelity surrogate models with near real-time latency. NVIDIA Modulus is a tool that can help you solve complex, nonlinear, multiphysics problems using AI. This tool provides the foundation for building physics machine learning surrogate models that combine physics and data. This framework can be applied to many domains and uses, including engineering simulations and life sciences. It can also be used to solve forward and inverse/data assimilation issues. Parameterized system representation that solves multiple scenarios in near real-time, allowing you to train once offline and infer in real-time repeatedly.

In less than 2 minutes, you can create a backend. Get a Postgres database, authentication and instant APIs to start your project. Real-time subscriptions are also available. You can build faster and concentrate on your products. Every project is a Postgres database, the most trusted relational database in the world. You can add user sign-ups or logins to secure your data with Row Level Security. Large files can be stored, organized and served. Any media, including images and videos. Without the need to deploy or scale servers, you can write custom code and cron jobs. There are many starter projects and example apps to help you get started. We will instantly inspect your database and provide APIs. Stop creating repetitive CRUD endpoints. Instead, focus on your product. Type definitions directly from your database schema. Supabase can be used in the browser without a build. You can develop locally and push to production as soon as you are ready. You can manage Supabase projects on your local machine.

Verifiable badges can be issued to acknowledge the qualifications, skills, and experience of a person, and to reward learning and achievement. eNetBadges, an Open Badges-compliant platform, allows you to create, issue, and manage digital badges. Badges are digital representations of a person’s credentials, abilities and competencies. They also include embedded metadata about the knowledge and activities that were involved in earning them. eNetBadges are a way to issue badges that can be verified for your micro-credentials. They can be used to encourage discovery, raise hiring practices, reward learning and achievement and incentivise learning.

Geospatial imaging professionals must process large amounts of geospatial information every day. They often rely on software that was designed for other purposes or add-on applications that create nearly as many problems than they solve. ERDAS IMAGINE allows you to save time and money, make better image analysis decisions, and leverage your data investments. ERDAS IMAGINE is a true value product that combines remote sensing, photogrammetry and LiDAR analysis with basic vector analysis and radar processing. ERDAS IMAGINE makes it easy to image classify and segment, orthorectification and mosaicking, reprojection and elevation extraction, as well as image interpretation. You can focus on your analysis by using powerful algorithms and data processing functions. ERDAS IMAGINE provides K-Means and ISODATA, as well as object-based image segmentation and Machine Learning.

A user-friendly interface will help you improve your embedding metadata, and embedding tokens. Apply advanced NLP cleaning techniques such as TF-IDF to normalize and enrich your embedded tokens. This will improve efficiency and accuracy for your LLM applications. Optimize relevance of content returned from vector databases by intelligently splitting and merging content based on structure, adding void or invisible tokens to make chunks more semantically coherent. Embedditor can be installed locally on your PC, in your enterprise cloud or on premises. Embedditor's advanced cleansing techniques can help you save up to 40% in embedding costs and vector storage by filtering out non-relevant tokens such as stop-words and punctuation.

Paradise employs robust, unsupervised machine-learning and supervised deep learning technologies in order to increase interpretation and gain greater insight from the data. Generate attributes to extract valuable geological information and for input into machine learning analysis. Identify the attributes that have the greatest variance and contribution to a given set of attributes in a particular geologic setting. Display the neural classes (topology), and the associated colors resulting Stratigraphic analysis. These indicate the distribution of facies. Deep learning and machine learning can automatically detect faults. Compare machine learning classification results with other seismic attributes to traditional logs. In fraction of the time it takes to generate spectral and geometric decomposition attributes on a cluster compute nodes, you can do this in fraction of the time with a single machine.

PostgresML is an entire platform that comes as a PostgreSQL Extension. Build simpler, faster and more scalable model right inside your database. Explore the SDK, and test open-source models in our hosted databases. Automate the entire workflow, from embedding creation to indexing and Querying for the easiest (and fastest) knowledge based chatbot implementation. Use multiple types of machine learning and natural language processing models, such as vector search or personalization with embeddings, to improve search results. Time series forecasting can help you gain key business insights. SQL and dozens regression algorithms allow you to build statistical and predictive models. ML at database layer can detect fraud and return results faster. PostgresML abstracts data management overheads from the ML/AI cycle by allowing users to run ML/LLM on a Postgres Database.

Machine learning can be shared across your organization by collaborating, democratizing, and operationalizing it. Data science is a team sport. Data scientists, citizen data scientists and data engineers, as well as business users and developers, need flexible tools that facilitate collaboration, automation and reuse of analytic workflows. Algorithms are just one part of advanced analytic technology. Companies must increase their focus on the management, deployment, and monitoring analytic models in order to deliver predictive insights. Smart businesses depend on platforms that can support the entire lifecycle of analytics and provide enterprise security and governance. TIBCO®, Data Science software allows organizations to innovate and solve complex problems more quickly, ensuring that predictive findings are quickly turned into optimal outcomes. Flexible authoring and deployment capabilities allow organizations to expand their data science deployments throughout the organization with TIBCO Data Science.

Build your RAG system intuitively with fewer abstractions than solutions like LangChain. You can choose from a variety of hosted and remote providers, including vector databases, datasets and Large Language Models. SciPhi allows you to version control and deploy your system from anywhere using Git. SciPhi's platform is used to manage and deploy an embedded semantic search engine that has over 1 billion passages. The team at SciPhi can help you embed and index your initial dataset into a vector database. The vector database will be integrated into your SciPhi workspace along with your chosen LLM provider.

Our clients use visual AI to solve real-world business challenges. Our skilled team of data scientists and machine-learning engineers will quickly train and deliver machine learning models that are highly accurate and precise for visual data. IceCream Labs, the leader in enterprise AI solutions, is a great choice. IceCream Labs offers solutions for retail, digital media, and higher education. The company's expertise lies in the development of machine learning and deep-learning models to solve real business problems using text and image data. IceCream Labs is a good choice if you have visual data such as images, videos and documents. We can help you identify what is in an image or document. IceCream Labs can help you quickly train and deploy machine learning models. Get sales performance improvements across all product lines by talking to our AI experts.

In recent years, the ability to convert text into pictures using artificial intelligence has attracted significant attention. Stable diffusion, which uses deep neural networks and their power to generate images from textual description, is an effective way to achieve this task. The first step is converting the textual description into a numerical format a neural networks can process. Text embedding, a popular technique, converts each word of the text into vector representation. After encoding the text, a deep-learning network creates an initial image using the encoded text. This image is noisy and lacks details, but it is a good starting point for the following step. The image is refined several times to improve its quality. Diffusion steps smooth and remove noise while preserving important details such as edges and contours.

Your reviewers don’t have to work harder, they just need to work smarter. Intelligent Supervision reduces the amount of "noise," which is easier for review teams to monitor and sort through. This allows you to spot compliance violations quicker and more accurately. The add-on to Intelligent Supervision is Proofpoint NexusAI Compliance. It can use past reviewer decisions to reduce low-value supervision content thanks to its machine learning models. Poor supervision can slow down regulatory response. Intelligent Supervision solves the problem in three powerful ways. It identifies bottlenecks and improves collaboration. It also boosts productivity to reduce compliance risk. All your archived content is provided with rich, visual reporting tools. You're always prepared to defend your company with actionable intelligence. Intelligent Supervision ensures that you are always ready to respond to any regulatory audit request at a moment’s notice.

Store the embeddings from previous attacks in a database of vectors to recognize and prevent them in the future. Use a dedicated LLM for analyzing incoming prompts to identify potential attacks. Add canary tokens in prompts to detect leakages. This allows the framework to store embedded embeddings of the incoming prompt into the vector database to prevent future attacks. Filter out malicious input before it reaches LLM.

AI can transform your semantic data into insight to guide your decisions and objectives. Synomia, a pioneer in Artificial Intelligence, is the owner of semantic data processing technology. Synomia transforms large amounts unstructured data into insight to help brands better target their strategies and activation systems. Analyse the market's strong and weak signals to identify tomorrow's trends. Find the most effective angles of attack to your digital strategies. We are experts in all aspects of semantic AI technology, which we activate depending on the needs of our clients: supervised or unsupervised computer learning and rule-based systems. Semantic AI allows you to analyze large amounts of data and create methodologies that are oriented towards novelty and discovery. It is the key to creating strategies that meet the expectations of your targets.

The MULTI Integrated Developer Environment (IDE), has been in continuous improvement for more than three decades. MULTI is trusted by developers to produce high-quality code, and help them get their devices to market quicker. MULTI works well, regardless of whether you are trying to find a bug, fix a memory leak or maximize system performance. Our revolutionary Debugger solves problems faster than traditional tools. It can take weeks or months to find the root cause of problems such as inter-task corruptions, missing real-time requirements, or external hardware events. The Green Hills TimeMachine tool suite can help you solve the same problems within hours or minutes. The TimeMachine tool suite automatically captures program execution information, combining the MULTI Debugger interface and innovative replay debugging capabilities.

Scheme is a general-purpose programming language for computers. It is a high level language that supports operations on structured data like strings, lists, vectors, and numbers, as well as operations with more traditional data like numbers and characters. Although Scheme is often associated with symbolic applications, it has a rich set of data types that can be used to create complex control structures and a wide range of other data types. Scheme can be used to create text editors, optimize compilers and graphics packages, expert system, numerical applications, financial analysis programs, virtual reality systems, as well as operating systems, graphics, expert systems, operating systems, graphics, expert systems, operating systems, graphic packages, optimization systems, programming languages, and other types of applications. Because it is based only on a few syntactic forms, semantic concepts, and because most implementations are interactive, Scheme is easy to learn. It is difficult to fully understand Scheme.

Elixir is an expressive, dynamic language that allows you to create scalable and maintainable applications. Elixir uses the ErlangVM, which is known for its low latency, distributed, fault-tolerant, and fault-tolerant system. Elixir has been successfully used in web development and embedded software. It also supports data ingestion and multimedia processing across many industries. To get started with Elixir, check out our getting started guide or our learning page. All Elixir code runs within lightweight threads of execution, also known as processes. These processes are isolated and exchange information through messages. Because they are lightweight, it is possible to have hundreds of thousands or more processes running simultaneously on the same machine. Isolation allows for processes to be separated, reducing system-wide pauses and making use of all machine resources as efficiently possible (vertical scaling). The process can also communicate with other processes on the same network.

The Iguazio MLOps Platform turns AI projects into real-world business results. You can accelerate and scale the development, deployment, and management of your AI apps with end-to–end automation of deep and machine learning pipelines. A fully integrated platform allows you to seamlessly deploy machine and deep learning models to high-powered business applications, reducing time to market and achieving real-time enterprise performance. Continuously and seamlessly deploy new model into business environments, monitor models during production, detect and mitigate drift, save time and money on operationalizing machine-learning, and save time. Automate and accelerate data science workflows so concepts flow smoothly from development through deployment to impact. Monitor Models, Detect Drift, and Auto-Trigger Training. You can deploy with ease to an Operational Pipeline.

Machine Learning made simple for everyone The leading Machine Learning platform will take your business to the next level. Get data-driven decisions now! No more cumbersome or expensive solutions. Machine Learning that works. BigML offers a variety of Machine Learning algorithms that are robustly engineered and can be applied across your company to solve real-world problems. You can avoid dependencies on multiple libraries that will increase complexity, maintenance costs, or technical debt in your projects. BigML allows unlimited predictive applications in all industries, including aerospace, automotive and energy, entertainment, financial, financial services, food and healthcare, IoT pharmaceutical, transportation, telecommunications and many more. Supervised Learning: Classification and regression (trees and ensembles, logistic regressions and deepnets), as well as time series forecasting.

Integrate analytics into real time interactions and event-based capabilities. SAS Visual Data Science Decisioning offers robust data management, visualization, advanced analysis, and model management. It supports decision making by creating, embedding, and governing analytically driven decision flows at scale in batch or real-time. It also provides analytics and stream-based decisions to help you uncover insights. A comprehensive visual interface allows you to solve complex analytical problems. It handles all aspects of the analytics lifecycle. SAS Visual Data Mining and Machine Learning runs in SAS®, Viya®. It combines data wrangling and exploration with feature engineering and modern statistical, data mining and machine learning techniques in one, scalable, in-memory processing environment. This web application is a development tool that you can access via your browser.

ParadeDB adds column-oriented storage to Postgres tables and vectorized query processing. Users can choose between column- and row-oriented storage when creating tables. Column-oriented tables can be stored as Parquet and managed by Delta Lake. Search by keyword, with BM25 scoring and configurable tokenizers. Multi-language support. Search by semantic meaning, with support for dense and sparse vectors. Combining the strengths of both full text and similarity searches, you can get better results. ParadeDB is ACID compliant and has concurrency control across all transactions. ParadeDB integrates seamlessly with the Postgres ecosystem including clients, extensions and libraries.

CVAT, the leading data engine for machine-learning, allows you to annotate better. Teams at all scales use and trust CVAT for data of all sizes. CVAT's intuitive, lightning-fast user interface was developed in collaboration with real-world teams that are solving real-world issues. CVAT is used by the most ambitious AI teams in the world every day, from medical to retail to autonomous cars. CVAT can handle any input data and expected results. It works well with images, videos and even 3D. Bounding boxes and polygons. Points, skeletons. Cuboids. Trajectories. Automated interactive algorithms such as intelligent scissors, histogram equality, and others will help you annotate more efficiently. Get actionable insights from metrics such as the annotator's working hours, the number of objects per hour and more.

ALBERT is a Transformer model that can be self-supervised and was trained on large amounts of English data. It does not need manual labelling and instead uses an automated process that generates inputs and labels from the raw text. It is trained with two distinct goals in mind. Masked Language Modeling is the first. This randomly masks 15% words in an input sentence and requires that the model predict them. This technique is different from autoregressive models such as GPT and RNNs in that it allows the model learn bidirectional sentence representations. Sentence Ordering Prediction is the second objective. This involves predicting the order of two consecutive text segments during pretraining.

NetOwl NameMatcher was the winner of the MITRE Multicultural Name Matching Challenge. It offers the fastest, most accurate, and scalable name match possible. NetOwl solves complex fuzzy name matching problems by using a machine learning-based approach. Traditional name matching methods such as Soundex edit distance and rule-based methods have problems with precision (false positivities) and recall (false negativities) when it comes to addressing the various fuzzy name matching challenges. NetOwl uses a machine learning-based probabilistic approach that is empirically driven to solve name matching problems. It automatically derives intelligent, probabilistic names matching rules from large-scale, real world, multi-ethnicity variant data. NetOwl uses different matching models that are optimized for each entity type (e.g., person or organization, place). NetOwl also performs automatic detection of name ethnicity.

All your customer management needs can be met with a fast and flexible decision engine. Paris is easy to use and adopt due to its intuitive user interface. Paris is a highly configurable and flexible decision engine system that allows for maximum flexibility and growth. You can implement models, both machine learning and traditional, across multiple products and decision science applications. These include marketing, credit scoring, customer management, collections, and marketing. It can be audited with all input, derived, and output variables available to any reporting suite. Software that is flexible and business-oriented Visualization and interaction with decision trees Analysis and design of an 'open box strategy'. Continuous improvement and strategy testing Route planning and decision making that is reliable and accurate. Multi-bureau and open bank support.

Our unique solution integrates with your existing infrastructure to provide instant analysis, detection, and response to cyber threats contained within encrypted data. Our advisory paper provides insight into the encrypted traffic issue and explains why TLS protocols and existing infrastructure pose security risks to your sensitive data. Learn how our unique solution uses the latest technology to make sure your business is secure, compliant with crypto standards, and delivers ROI. All encrypted data packets are extracted in real-time and metadata is forwarded to Barac for analysis. Unique AI that uses machine learning and behavioral analytics (involving 200+ metrics), detects known threat vectors to identify potential threats. For immediate response, alerts are sent to the SIEM, SOC or alternative security team.

HPE Ezmeral ML Ops offers pre-packaged tools that enable you to operate machine learning workflows at any stage of the ML lifecycle. This will give you DevOps-like speed, agility, and speed. You can quickly set up environments using your preferred data science tools. This allows you to explore multiple enterprise data sources, and simultaneously experiment with multiple deep learning frameworks or machine learning models to find the best model for the business problems. On-demand, self-service environments that can be used for testing and development as well as production workloads. Highly performant training environments with separation of compute/storage that securely access shared enterprise data sources in cloud-based or on-premises storage.

The Exa API uses embeddings to search for the best content available on the web. Exa understands meaning and gives results that search engines cannot. Exa uses a new link prediction transformer to predict the links that match the meaning of an prompt. Search with our SOTA web embeddeddings model instead of our custom index for queries that require semantic understanding. We offer keyword-based searches for all other queries. Stop learning HTML parsing or web scraping. Get the full, clean text of any page from our index or intelligently embedded highlights related to your query. You can select any date range and include or exclude any domain. You can also choose a custom data vertical or get up 10 million results.