Embeddinghub Alternatives

Long-term memory for AI. The Pinecone vector database makes it easy to build high-performance vector search applications. Developer-friendly, fully managed, and easily scalable without infrastructure hassles. Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval. Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results. Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely.

Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values.

Zilliz Cloud is a fully managed vector database based on the popular open-source Milvus. Zilliz Cloud helps to unlock high-performance similarity searches with no previous experience or extra effort needed for infrastructure management. It is ultra-fast and enables 10x faster vector retrieval, a feat unparalleled by any other vector database management system. Zilliz includes support for multiple vector search indexes, built-in filtering, and complete data encryption in transit, a requirement for enterprise-grade applications. Zilliz is a cost-effective way to build similarity search, recommender systems, and anomaly detection into applications to keep that competitive edge.

LlamaIndex is a “data framework” to help you build LLM apps. Connect semi-structured data from API's like Slack, Salesforce, Notion, etc. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. LlamaIndex provides the key tools to augment your LLM applications with data. Connect your existing data sources and data formats (API's, PDF's, documents, SQL, etc.) to use with a large language model application. Store and index your data for different use cases. Integrate with downstream vector store and database providers. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge-augmented response. Connect unstructured sources such as documents, raw text files, PDF's, videos, images, etc. Easily integrate structured data sources from Excel, SQL, etc. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.

Marqo is more than a vector database, it's an end-to-end vector search engine. Vector generation, storage, and retrieval are handled out of the box through a single API. No need to bring your own embeddings. Accelerate your development cycle with Marqo. Index documents and begin searching in just a few lines of code. Create multimodal indexes and search combinations of images and text with ease. Choose from a range of open source models or bring your own. Build interesting and complex queries with ease. With Marqo you can compose queries with multiple weighted components. With Marqo, input pre-processing, machine learning inference, and storage are all included out of the box. Run Marqo in a Docker image on your laptop or scale it up to dozens of GPU inference nodes in the cloud. Marqo can be scaled to provide low-latency searches against multi-terabyte indexes. Marqo helps you configure deep-learning models like CLIP to pull semantic meaning from images.

Vector database built for scalable similarity search. Open-source, highly scalable, and blazing fast. Store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning (ML) models. With Milvus vector database, you can create a large-scale similarity search service in less than a minute. Simple and intuitive SDKs are also available for a variety of different languages. Milvus is hardware efficient and provides advanced indexing algorithms, achieving a 10x performance boost in retrieval speed. Milvus vector database has been battle-tested by over a thousand enterprise users in a variety of use cases. With extensive isolation of individual system components, Milvus is highly resilient and reliable. The distributed and high-throughput nature of Milvus makes it a natural fit for serving large-scale vector data. Milvus vector database adopts a systemic approach to cloud-nativity, separating compute from storage.

Combine semantic relevance and user feedback to reliably retrieve the optimal document chunks in your retrieval augmented generation system. Combine semantic relevance and document freshness in your search system, because more recent results tend to be more accurate. Build a real-time personalized ecommerce product feed with user vectors constructed from SKU embeddings the user interacted with. Discover behavioral clusters of your customers using a vector index in your data warehouse. Describe and load your data, use spaces to construct your indices and run queries - all in-memory within a Python notebook.

Metal is your production-ready, fully-managed, ML retrieval platform. Use Metal to find meaning in your unstructured data with embeddings. Metal is a managed service that allows you to build AI products without the hassle of managing infrastructure. Integrations with OpenAI, CLIP, and more. Easily process & chunk your documents. Take advantage of our system in production. Easily plug into the MetalRetriever. Simple /search endpoint for running ANN queries. Get started with a free account. Metal API Keys to use our API & SDKs. With your API Key, you can use authenticate by populating the headers. Learn how to use our Typescript SDK to implement Metal into your application. Although we love TypeScript, you can of course utilize this library in JavaScript. Mechanism to fine-tune your spp programmatically. Indexed vector database of your embeddings. Resources that represent your specific ML use-case.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. 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. 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. Users can easily build recommendation applications on Vespa. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. 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.

Chroma is an AI-native open-source embedding database. Chroma has all the tools you need to use embeddings. Chroma is building the database that learns. Pick up an issue, create a PR, or participate in our Discord and let the community know what features you would like.

Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research.

LanceDB is a developer-friendly, open source database for AI. From hyperscalable vector search and advanced retrieval for RAG to streaming training data and interactive exploration of large-scale AI datasets, LanceDB is the best foundation for your AI application. Installs in seconds and fits seamlessly into your existing data and AI toolchain. An embedded database (think SQLite or DuckDB) with native object storage integration, LanceDB can be deployed anywhere and easily scales to zero when not in use. From rapid prototyping to hyper-scale production, LanceDB delivers blazing-fast performance for search, analytics, and training for multimodal AI data. Leading AI companies have indexed billions of vectors and petabytes of text, images, and videos, at a fraction of the cost of other vector databases. More than just embedding. Filter, select, and stream training data directly from object storage to keep GPU utilization high.

Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. Whether you bring your own vectors or use one of the vectorization modules, you can index billions of data objects to search through. Combine multiple search techniques, such as keyword-based and vector search, to provide state-of-the-art search experiences. Improve your search results by piping them through LLM models like GPT-3 to create next-gen search experiences. Beyond search, Weaviate's next-gen vector database can power a wide range of innovative apps. Perform lightning-fast pure vector similarity search over raw vectors or data objects, even with filters. Combine keyword-based search with vector search techniques for state-of-the-art results. Use any generative model in combination with your data, for example to do Q&A over your dataset.

Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed. Usually the graph requires locking during indexing, which cause stop-the-world. But Vald uses distributed index graph so it continues to work during indexing. Vald implements its own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery.

Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.

Atlas integrates into your workflow by organizing text and embedding datasets into interactive maps for exploration in a web browser. You shouldn’t have to scroll through Excel files, log Dataframes and page through lists to understand your data. Atlas automatically reads, organizes and summarizes your collections of documents surfacing trends and patterns. Atlas’ pre-organized data interface allows you to quickly surface pathologies and dirty data that can jeopardize your AI projects. Label and tag your data while you clean it with immediate sync to your Jupyter Notebook. Vector databases enable powerful applications such as recommendation systems but are notoriously hard to interpret. Atlas stores, visualizes and lets you search through all of your vectors in the same API.

Semantee is a hassle-free easily configurable managed database optimized for semantic search. It is provided as a set of REST APIs, which can be integrated into any app in minutes and offers multilingual semantic search for applications of virtually any size both in the cloud and on-premise. The product is priced significantly more transparently and cheaply compared to most providers and is especially optimized for large-scale apps. 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.

Open-source vector similarity search for Postgres. Supports exact and approximate nearest neighbor search for L2 distance, inner product, and cosine distance.

Deliver high-quality responses with a vector database built for advanced retrieval augmented generation (RAG) and modern search. Focus on exponential growth with an enterprise-ready vector database that comes with security, compliance, and responsible AI practices built in. Build better applications with sophisticated retrieval strategies backed by decades of research and customer validation. Quickly deploy your generative AI app with seamless platform and data integrations for data sources, AI models, and frameworks. Automatically upload data from a wide range of supported Azure and third-party sources. Streamline vector data processing with built-in extraction, chunking, enrichment, and vectorization, all in one flow. Support for multivector, hybrid, multilingual, and metadata filtering. Move beyond vector-only search with keyword match scoring, reranking, geospatial search, and autocomplete.

Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands.

KDB.AI is a powerful knowledge-based vector database and search engine that allows developers to build scalable, reliable and real-time applications by providing advanced search, recommendation and personalization for AI applications. Vector databases are a new wave of data management designed for generative AI, IoT and time-series applications. Here's why they matter, what makes them different, how they work, the new use cases they're designed for, and how to get started.

Astra DB from DataStax is vector database for developers that need to get accurate Generative AI applications into production, quickly and efficiently. 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 and SAML SSO secure account accessibility. You can deploy on AWS, GCP, or Azure while still maintaining open-source Cassandra compatibility.

The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data.

MyScale is an innovative AI database that seamlessly integrates vector search with SQL analytics, delivering a comprehensive, fully managed, and high-performance solution. Key Features: - Superior Data Capacity and Performance: Each MyScale pod supports 5 million 768-dimensional data points with exceptional accuracy, enabling over 150 queries per second (QPS). - Rapid Data Ingestion: Import up to 5 million data points in under 30 minutes, reducing waiting time and enabling faster utilization of your vector data. - Flexible Indexing: MyScale allows you to create multiple tables with unique vector indexes, efficiently managing diverse vector data within a single cluster. - Effortless Data Import and Backup: Seamlessly import/export data from/to S3 or other compatible storage systems, ensuring smooth data management and backup processes. With MyScale, unleash the power of advanced AI database capabilities for efficient and effective data analysis.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

Create a backend in less than 2 minutes. Start your project with a Postgres database, authentication, instant APIs, real-time subscriptions and storage. Build faster and focus on your products. Every project is a full Postgres database, the world's most trusted relational database. Add user sign-ups and logins, securing your data with Row Level Security. Store, organize and serve large files. Any media, including videos and images. Write custom code and cron jobs without deploying or scaling servers. There are many example apps and starter projects to get going. We introspect your database to provide APIs instantly. Stop building repetitive CRUD endpoints and focus on your product. Type definitions built directly from your database schema. Use Supabase in the browser without a build process. Develop locally and push to production when you're ready. Manage Supabase projects from your local machine.

NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly.

Geographic imaging professionals need to process vast amounts of geospatial data every day — often relying on software designed for other purposes and add-on applications that create almost as many problems as they solve. Save both time and money, leverage existing data investments, and improve your image analysis capabilities with ERDAS IMAGINE. ERDAS IMAGINE provides true value, consolidating remote sensing, photogrammetry, LiDAR analysis, basic vector analysis, and radar processing into a single product. ERDAS IMAGINE simplifies image classification and segmentation, orthorectification, mosaicking, reprojection, elevation extraction, and image interpretation. Powerful algorithms and data processing functions work behind the scenes so you can concentrate on your analyses. ERDAS IMAGINE offers K-Means, ISODATA, object-based image segmentation, Machine Learning and Deep Learning Artificial Intelligence algorithms.

Issue verifiable badges to acknowledge a person's credentials, abilities, experiences and competencies - and to recognise and reward learning and achievement. eNetBadges is an Open Badges compliant platform for creating, issuing, and managing digital badges. Badges are a digital representation of a person's credentials, abilities, experiences and competencies and they include embedded metadata about the knowledge and activities it took to earn them. eNetBadges can be used to issue verifiable badges for your micro-credentials and are used to foster discovery, elevate hiring practices, promote engagement, reward learning and achievement, drive the acquisition of knowledge and skills, and to incentivise learning.

Improve your embedding metadata and embedding tokens with a user-friendly UI. Seamlessly apply advanced NLP cleansing techniques like TF-IDF, normalize, and enrich your embedding tokens, improving efficiency and accuracy in your LLM-related applications. Optimize the relevance of the content you get back from a vector database, intelligently splitting or merging the content based on its structure and adding void or hidden tokens, making chunks even more semantically coherent. Get full control over your data, effortlessly deploying Embedditor locally on your PC or in your dedicated enterprise cloud or on-premises environment. Applying Embedditor advanced cleansing techniques to filter out embedding irrelevant tokens like stop-words, punctuations, and low-relevant frequent words, you can save up to 40% on the cost of embedding and vector storage while getting better search results.

Paradise uses robust, unsupervised machine learning and supervised deep learning technologies to accelerate interpretation and generate greater insights from the data. Generate attributes to extract meaningful geological information and as input into machine learning analysis. Identify attributes having the highest variance and contribution among a set of attributes in a geologic setting, Display the neural classes (topology) and their associated colors resulting from Stratigraphic Analysis that indicate the distribution of facies. Detect faults automatically with deep learning and machine learning processes. Compare machine learning classification results and other seismic attributes to traditional good logs. Generate geometric and spectral decomposition attributes on a cluster of compute nodes in a fraction of the time on a single machine.

Democratize, collaborate, and operationalize, machine learning across your organization. Data science is a team sport. Data scientists, citizen data scientists, data engineers, business users, and developers need flexible and extensible tools that promote collaboration, automation, and reuse of analytic workflows. But algorithms are only one piece of the advanced analytic puzzle. To deliver predictive insights, companies need to increase focus on the deployment, management, and monitoring of analytic models. Smart businesses rely on platforms that support the end-to-end analytics lifecycle while providing enterprise security and governance. TIBCO® Data Science software helps organizations innovate and solve complex problems faster to ensure predictive findings quickly turn into optimal outcomes. TIBCO Data Science allows organizations to expand data science deployments across the organization by providing flexible authoring and deployment capabilities.

PostgresML is a complete platform in a PostgreSQL extension. Build simpler, faster, and more scalable models right inside your database. Explore the SDK and test open source models in our hosted database. Combine and automate the entire workflow from embedding generation to indexing and querying for the simplest (and fastest) knowledge-based chatbot implementation. Leverage multiple types of natural language processing and machine learning models such as vector search and personalization with embeddings to improve search results. Leverage your data with time series forecasting to garner key business insights. Build statistical and predictive models with the full power of SQL and dozens of regression algorithms. Return results and detect fraud faster with ML at the database layer. PostgresML abstracts the data management overhead from the ML/AI lifecycle by enabling users to run ML/LLM models directly on a Postgres database.

We ​help our clients ​leverage visual AI to solve real-world business problems​. Our team of skilled data scientists and machine learning engineers ​will quickly train and deliver highly precise and accurate machine learning models for your visual data. IceCream Labs is the leading enterprise AI solution company. IceCream Labs provides solutions for retail, digital media and higher education. The company’s expertise is developing machine learning and deep learning models to solve real world business problems using text, image and numerical data. Try IceCream Labs if your business ​handles visual data like images, video and documents. If you need to identify what’s in an image or a document, we can help you. ​If you need to quickly train and deploy a machine learning model, IceCream Labs is the answer. Talk to our AI experts and get sales performance improvements across your product line.

Intuitively build your RAG system with fewer abstractions compared to solutions like LangChain. Choose from a wide range of hosted and remote providers for vector databases, datasets, Large Language Models (LLMs), application integrations, and more. Use SciPhi to version control your system with Git and deploy from anywhere. The platform provided by SciPhi is used internally to manage and deploy a semantic search engine with over 1 billion embedded passages. The team at SciPhi will assist in embedding and indexing your initial dataset in a vector database. The vector database is then integrated into your SciPhi workspace, along with your selected LLM provider.

The ability to convert text into images using artificial intelligence has gained significant attention in recent years. Stable diffusion is one effective method for achieving this task, utilizing the power of deep neural networks to generate images from textual descriptions. The first step is to convert the textual description of an image into a numerical format that a neural network can process. Text embedding is a popular technique that converts each word in the text into a vector representation. After encoding, a deep neural network generates an initial image based on the encoded text. This image is usually noisy and lacks detail, but it serves as a starting point for the next step. The generated image is refined in several iterations to improve the quality. Diffusion steps are applied gradually, smoothing and removing noise while preserving important features such as edges and contours.

Your reviewers don’t need to work harder; they need to work smarter. Intelligent Supervision creates less “noise” for your review teams to monitor and sift through. That means you can pinpoint compliance violations faster and more accurately. Proofpoint NexusAI for Compliance is an add-on to Intelligent Supervision. With its machine learning models, it can use past reviewer decisions to help significantly reduce your low-value supervision content. Poor monitoring in the supervision process can slow regulatory response. Intelligent Supervision solves this problem in three powerful ways: It identifies bottlenecks, improves collaboration, and boosts productivity to reduce compliance risks. You get rich, visual reporting tools for all your archived content. And armed with actionable intelligence, you’re always at the ready to protect your firm. Intelligent Supervision keeps you ready to respond to regulatory audit requests at a moment’s notice.

Thanks to AI, transform your semantic data into insights to objectify your strategic decisions and guide your actions. A pioneer in Artificial Intelligence and owner of semantic data processing technologies, Synomia transforms large amounts of unstructured data into insights to enable brands to better objectify their strategies and activation systems. Identify tomorrow's trends based on the massive analysis of strong and weak signals in your market. Find the most impactful angles of attack for your digital strategies. We master all semantic AI technologies, which we activate according to the needs of our customers: supervised or unsupervised machine learning and rule-based systems. Semantic AI makes it possible to analyze a large number of sources and makes it possible to set up methodologies oriented towards discovery and novelty, it is the key to strategies truly aligned with the expectations of its targets.

Store embeddings of previous attacks in a vector database to recognize and prevent similar attacks in the future. Use a dedicated LLM to analyze incoming prompts and identify potential attacks. Add canary tokens to prompts to detect leakages, allowing the framework to store embeddings about the incoming prompt in the vector database and prevent future attacks. Filter out potentially malicious input before it reaches the LLM.

Scheme is a general-purpose computer programming language. It is a high-level language, supporting operations on structured data such as strings, lists, and vectors, as well as operations on more traditional data such as numbers and characters. While Scheme is often identified with symbolic applications, its rich set of data types and flexible control structures make it a truly versatile language. Scheme has been employed to write text editors, optimize compilers, operating systems, graphics packages, expert systems, numerical applications, financial analysis packages, virtual reality systems, and practically every other type of application imaginable. Scheme is a fairly simple language to learn since it is based on a handful of syntactic forms and semantic concepts and since the interactive nature of most implementations encourages experimentation. Scheme is a challenging language to understand fully.

After more than three decades of customer use and continuous enhancement, the MULTI Integrated Development Environment (IDE) is unmatched in the embedded software industry. Developers know they can rely on MULTI to help them produce high-quality code and get their devices to market faster. Whether pinpointing a hard-to-find bug, resolving a memory leak, or maximizing system performance, MULTI consistently works. Every feature of our revolutionary Debugger is designed to quickly solve problems that stump traditional tools. It often takes weeks or months to track down problems like inter-task corruptions, missed real-time requirements, and external hardware events. Green Hills' TimeMachine tool suite helps you solve the same problems in hours or even minutes. The TimeMachine tool suite automatically captures program execution data, combining the MULTI Debugger interface with innovative replay debugging capabilities.

Elixir is a dynamic, functional language for building scalable and maintainable applications. Elixir leverages the Erlang VM, known for running low-latency, distributed, and fault-tolerant systems. Elixir is successfully used in web development, embedded software, data ingestion, and multimedia processing, across a wide range of industries. Check our getting started guide and our learning page to begin your journey with Elixir. All Elixir code runs inside lightweight threads of execution (called processes) that are isolated and exchange information via messages. Due to their lightweight nature, it is not uncommon to have hundreds of thousands of processes running concurrently in the same machine. Isolation allows processes to be garbage collected independently, reducing system-wide pauses, and using all machine resources as efficiently as possible (vertical scaling). Processes are also able to communicate with other processes running on different machines in the same network.

Integrate analytics into real-time ​interactions and event-based capabilities​. SAS Visual Data Science Decisioning features robust data management, visualization, advanced analytics and model management. It supports decisions by creating, embedding and governing analytically driven decision flows at scale in real-time or batch. It also deploys analytics and decisions in the stream to help you discover insights. Solve complex analytical problems with a comprehensive visual interface that handles all tasks in the analytics life cycle. SAS Visual Data Mining and Machine Learning, which runs in SAS® Viya®, combines data wrangling, exploration, feature engineering, and modern statistical, data mining, and machine learning techniques in a single, scalable in-memory processing environment. Access data files, libraries and existing programs, or write new ones, with this developmental web application accessible through your browser.

Machine Learning made beautifully simple for everyone. Take your business to the next level with the leading Machine Learning platform. Start making data-driven decisions today! No more wildly expensive or cumbersome solutions. Machine Learning that simply works. BigML provides a selection of robustly-engineered Machine Learning algorithms proven to solve real world problems by applying a single, standardized framework across your company. Avoid dependencies on many disparate libraries that increase complexity, maintenance costs, and technical debt in your projects. BigML facilitates unlimited predictive applications across industries including aerospace, automotive, energy, entertainment, financial services, food, healthcare, IoT, pharmaceutical, transportation, telecommunications, and more. Supervised Learning: classification and regression (trees, ensembles, linear regressions, logistic regressions, deepnets), and time series forecasting.

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. CVAT’s blazing-fast, intuitive user interface, was designed by working closely with real-world teams solving real-world problems. From medical to retail to autonomous vehicles, world’s most ambitious AI teams use CVAT as a part of their AI workflow every day. No matter what your input data or expected results are, CVAT is ready. It works great with images, videos, and even 3D. Bounding boxes, polygons, points, skeletons, cuboids, trajectories, and more. Annotate more efficiently with automated interactive algorithms like intelligent scissors, histogram equalization, and more. Gain actionable insights with metrics such as annotator working hours, objects per hour, and more.

NetOwl NameMatcher, the winner of the MITRE Multicultural Name Matching Challenge, offers the most accurate, fast, and scalable name matching available. Using a revolutionary machine learning-based approach, NetOwl addresses complex fuzzy name matching challenges. Traditional name matching approaches, such as Soundex, edit distance, and rule-based methods, suffer from both precision (false positives) and recall (false negative) problems in addressing the variety of fuzzy name matching challenges discussed above. NetOwl applies an empirically driven, machine learning-based probabilistic approach to name matching challenges. It derives intelligent, probabilistic name matching rules automatically from large-scale, real-world, multi-ethnicity name variant data. NetOwl utilizes different matching models optimized for each of the entity types (e.g., person, organization, place) In addition, NetOwl performs automatic name ethnicity detection as well.

ParadeDB brings column-oriented storage and vectorized query execution to Postgres tables. Users can choose between row and column-oriented storage at table creation time. Column-oriented tables are stored as Parquet files and are managed by Delta Lake. Search by keyword with BM25 scoring, configurable tokenizers, and multi-language support. Search by semantic meaning with support for sparse and dense vectors. Surface results with higher accuracy by combining the strengths of full text and similarity search. ParadeDB is ACID-compliant with concurrency controls across all transactions. ParadeDB integrates with the Postgres ecosystem, including clients, extensions, and libraries.

ALBERT is a self-supervised Transformer model that was pretrained on a large corpus of English data. This means it does not require manual labelling, and instead uses an automated process to generate inputs and labels from raw texts. It is trained with two distinct objectives in mind. The first is Masked Language Modeling (MLM), which randomly masks 15% of words in the input sentence and requires the model to predict them. This technique differs from RNNs and autoregressive models like GPT as it allows the model to learn bidirectional sentence representations. The second objective is Sentence Ordering Prediction (SOP), which entails predicting the ordering of two consecutive segments of text during pretraining.

Our unique solution works with your existing infrastructure to deliver instant analysis, detection and response to cyber threats carried within your encrypted data. Read our advisory paper, get insight into the encrypted traffic problem and understand why the use of TLS protocols and your existing infrastructure are raising the security risks for your critical data. Then read how our unique solution utilises the latest technology to ensure your business is cyber secure, crypto compliant and delivering ROI. Metadata is extracted from all incoming/outgoing encrypted data packets in real time, and forwarded to the Barac platform for analysis. Unique AI utilising machine learning and behavioural analytics (involving 200+ metrics) detects known threat vectors and abnormal traffic to discover potential threats. Alerts are sent to your specified security team SOC, SIEM or alternative, for immediate response.

A fast and flexible decision engine for all your customer management needs. An intuitive, point-and-click user experience makes Paris easy to adopt and use for implementing scorecards and decision science in credit, fraud risk, marketing, and more. Designed for maximum flexibility and growth, Paris is a highly scalable and configurable decision engine system. Implement models – both traditional and machine learning – and complex rules, across numerous products and decision science applications, including marketing, application scoring, credit strategies, customer management and collections. It is fully auditable, with all input, derived and output variables available to the reporting suite. Business-led, flexible software. Visualization of and interaction with decision trees. ‘What if?’ analysis and ‘open box’ strategy design. Strategy testing and continuous improvement. Reliable and accurate decisioning and routing. Multi-bureau and open banking support.