Mercia Equity Finance is a regional investor that focuses initially on uncovering the hidden value. It uses both of its managed funds and its proprietary capital to invest across four distinct asset classes: venture, private equity, and debt, referred to as their 'Complete Capital Solution’. It aspires to be the first choice for investors, investees, and employees, and, by aspiring to be the first choice, they believe that Mercia will deliver superior returns for business owners, shareholders, and fund investors alike.

NPIF – Mercia Equity Finance

In-Q-Tel identifies and partners with companies developing cutting-edge technologies to help deliver these solutions to the Central Intelligence Agency and the broader U.S. Intelligence Community (IC) to further their missions.

In-Q-Tel

Deeptech Labs is a VC fund, accelerator, and catalyst for deep tech success.

Deeptech Labs

Mindtech

Mindtech's Data Ops Platform is designed to massively reduce the time ML engineers spend collecting and curating training data for AI computer vision applications. The platform is highly automated with embedded functionality, leveraging AI and Mindtechs unique Configurable DNA technology, to support use cases in multiple industries' such as Smart City (transportation, traffic, and people management) Smart Places (manufacturing, retail, construction, hospital, and office environments) and Smart Machines (robotics, automotive, marine and UAV). Mindtech's Data Ops Platform analyses the customers training data and then automates the creation of new synthetic training data including pixel accurate annotations from environments representative of the customers deployment environment. Fast, flexible, repeatable, delivering hi fidelity images Mindtech's Data Ops Platform is the ultimate solution to delivering high performance, production ready AI models for computer vision.

Mindtech provides a no-code platform called Chameleon that leverages automated behavioral simulators to create and curate annotated synthetic data to train AI computer vision models. It also supports <a href="https://sp-edge.com/updates/29705">LiDAR (light detection and ranging) and NIR (near-infrared)</a> simulation capabilities, allowing AI developers to create Lidar and NIR training and test data. The platform enables users to import artwork using 3D file formats and convert them into smart objects. The Scenario Editor feature enables drag-and-drop functionality to generate files that can be manipulated using programming languages, such as Python. Additionally, the Curation Manager allows users to review and analyze datasets, and helps target future simulations for appropriate diversity and bias.

The toolkit to build machine learning applications at scale

Machine Learning Infrastructure

Mindtech provides a no-code platform called Chameleon that leverages automated behavioral simulators to create and curate annotated synthetic data to train AI computer vision models. It also supports <a href="https://sp-edge.com/updates/29705">LiDAR (light detection and ranging) and NIR (near-infrared)</a> simulation capabilities, allowing AI developers to create Lidar and NIR training and test data. The platform enables users to import artwork using 3D file formats and convert them into smart objects. The Scenario Editor feature enables drag-and-drop functionality to generate files that can be manipulated using programming languages, such as Python. Additionally, the Curation Manager allows users to review and analyze datasets, and helps target future simulations for appropriate diversity and bias.The platform can be used to create synthetic data for industries such as automotive, logistics, manufacturing, smart homes and cities, and retail.Key customers and partnershipsMindtech partnered with data company Appen in March 2022 to collaboratively deliver a range of real and synthetic images and related data and metadata annotation solutions.Funding and financialsIn March 2022, the company raised USD 3.7 million in a funding round led by Appen. The funds were utilized to support its rapid growth.

Datagen provides a self-service synthetic data platform that generates visual synthetic data of humans for computer vision applications. The platform allows users to write their own code to generate labeled synthetic data for faces, full body, and humans in motion in the form of images and videos to test and train AI models while retaining complete control over the data. The data covers a range of computer vision tasks including face landmark detection, face and hair segmentation, facial recognition and identification, full body pose estimation, full body segmentation, and behavior detection.Key customers and partnershipsThe company serves three of the top five global technology companies and Fortune 500 companies across the automotive, smart fitness, AR/VR/metaverse, smart office, and home security industries. Its notable customers include Google, Toyota, NVIDIA, IBM, and Intel.Funding and financialsIn March 2022, the company raised USD 50 million in a Series B funding round led by Scale Venture Partners. The funds were utilized to bolster its position in the market.Datagen reported to have recorded 8x growth in revenue in 2022.

Datagen

Weights &amp; Biases offers a platform that allows users to build models with experiment tracking, data set and model versioning, data visualization, hyperparameter optimization, and model management. The platform enables users to automatically retrain and re-evaluate models and centrally manage all model versions through every lifecycle stage—from development to staging to production. It also helps to visualize comparisons between model versions to evaluate performance and identify regressions before model deployment. It also offers toolkits such as <a href="https://sp-edge.com/updates/19172">W&amp;B Weave and W&amp;B Production Monitoring </a>to build and monitor data- and ML-powered applications, respectively, through interactive visualizations.The company also offers a <a href="https://sp-edge.com/updates/17880">suite of tools (W&amp;B Prompts) </a>to support large language model operations (LLMOps). W&amp;B Prompts includes tools and features for experiment tracking, debugging, and collaboration, as well as a one-click method to evaluate and score prompts through the OpenAI Eval’s framework.The company provides the platform through a free tier and under a subscription model for up to USD 50 per user per month.Key customers and partnershipsWeights &amp; Biases served more than 700,000 ML practitioners as of August 2023. Its notable include NVIDIA, Toyota, BMW Group, Samsung, Fujifilm, Truebill, and Clear Capital.The company has <a href="https://sp-edge.com/updates/18663">partnered with Lambda </a>, a GPU cloud computing provider, to offer its MLOps platform integrated with the infrastructure services of the latter, catering to enterprises dealing with AI and deep-learning workloads.Funding and financialsIn August 2023, the company raised <a href="https://sp-edge.com/updates/20874">USD 50 million in a strategic investment </a>round at a valuation of USD 1.25 billion, led by Daniel Gross and Nat Friedman. The round also saw participation from Sapphire Ventures, Coatue, Insight Partners, and other investors. The company aimed to utilize the fresh funds to further develop its ML tools, including the launch of W&amp;B Prompts.

Weights & Biases

BentoML offers an open-source platform called Bento Cloud, for the deployment and scaling of AI applications. It is a fully managed platform that handles the complexities of AI infrastructure, providing serverless autoscaling as required during the development and production stages. The platform helps to ship models to production with advanced deployment options including online and shadow A/B testing, while also providing visibility through model performance monitoring. The platform integrates with an organization’s existing data stack and enables users to continuously deploy and improve models in production. The company offers its platform through a combination of subscription and usage-based model.In addition, BentoML also provides the following open-source platforms: 1) Yatai, for model deployment on Kubernetes and 2) bentoctl, for model deployment on any cloud platform.Key customers and partnershipsAs of January 2024, BentoML reported that over 1,000 organizations, comprising more than 3,000 members, use its platform. Its notable customers include Koo, Open Layer, Line, and Naver.The company entered into a strategic partnership with TomTom, a developer of location technology, to help integrate with various models as well as in LLM deployments.Funding and financialsThe company raised <a href="https://sp-edge.com/updates/19684">USD 9 million in seed funding </a>in June 2023 led by DCM Ventures, with participation from Bow Capital. It aimed to channel the newly acquired funds to enhance its platform and offer developers a range of tools for building, deploying, and scaling AI applications efficiently.

BentoML

Iguazio provides a serverless platform based on its open-source Nuclio framework that helps users to develop, deploy, and manage real-time AI applications without requiring them to manually manage underlying infrastructures such as servers and storage. The platform creates real-time intelligence for applications located at the edge, public cloud, and on-premise data centers and reduces bandwidth constraints by consolidating data into a repository and making it available for applications as required. The platform can ingest and transform data from any source, which is then used to train, evaluate, deploy, and monitor models in production.The company leverages Big Data and ML software such as Apache Spark, TensorFlow, and Kubernetes to manage software containers that enable applications to run across different computing environments.Key customers and partnershipsIguazio serves customers in the financial services, telecommunications, smart mobility, manufacturing, retail, AdTech, gaming, healthcare, energy, and utilities sectors. Notable customers include Samsung, Microsoft, NVIDIA, AWS, Google, and Intel.Funding and financialsIn January 2020, the company raised USD 24 million in a funding round led by INCapital Ventures. The funds were utilized to accelerate its growth, expand its reach into new global markets, grow sales, and facilitate new sales channels that include companies such as Dell Technologies

Iguazio

Cogito Tech provides image and video annotation to generate computer vision training data sets for ML &amp; deep learning (DL). The video annotation solution leverages AI-powered tools to convert video into a sequence of images and generates training data sets for ML-based AI models, including self-driving cars and drones. The AI model understands and derives insights from visual data to detect, identify, classify, and track objects in an image or video. Cogito also provides virtual assistants and chatbots to help users in the process of training ML algorithms. Cogito Tech also provides high-quality, ethically sourced datasets that are compliant and reliable to train ML models.In addition, the company provides services to fine-tune LLMs and GenAI applications through reinforcement learning techniques as well as stress test models through red teaming.Key customers and partnershipsAs of December 2022, the company served more than 150 customers across 25 countries in the retail, medical, automotive, agriculture, robotics, smart cities, security, and manufacturing industries. Notable customers includes Verizon Media, The University of Sydney, National Geographic, Tumblr, and Vimeo.

Cogito Tech

Platforms and frameworks that help developers build and train ML models.

Platforms that deploy and host the trained and tested models in production and integrate them into applications through APIs.

Platforms that generate artificial data that mimics the characteristics of real data, which is then used to train ML models at scale.

Platforms that help monitor and manage ML models in training and deployment to track performance, conduct variance analysis, and optimize performance.

Makers of hardware such as CPUs, GPUs, and other application-specific hardware required to train and run ML workloads.

Integrated solutions that handle the end-to-end processes of developing, training, deploying, and monitoring ML models.

Data exchange providers and platforms that offer data labeling services via manual and/or software methods, which are used as input to train ML models.

Includes companies that provide infrastructure to build AI/ML applications

Provides tools to build, monitor, and deploy GenAI applications

Industry is focused on offering platforms for other businesses to build AI-/ML-powered apps

Data Infrastructure & Analytics

ANALYST_QUICK_TAKE

PROMINENT_CATEGORY

<div>

 Snowflake, a firm focused on cloud-based data and artificial intelligence (AI) infrastructure, has announced partnerships with multiple AI companies at the Snowflake Data Cloud Summit 2024. The objective of these collaborations is to provide AI and machine learning (ML) development capabilities to Snowflake's customers via the Snowflake Marketplace.
 The company has teamed up with Mindtech, a platform that generates synthetic data. This partnership sees the launch of synthetic data training packs on Snowflake Marketplace, allowing joint customers to use synthetic data document packs to train AI vision systems quickly and with greater accuracy for particular applications.
 Snowflake has also formed a partnership with Tonic.ai, another platform that produces synthetic data. Together, they have released the Tonic Textual Native App on Snowflake Marketplace. The application ensures seamless and secure data redaction, tokenization, and synthesis of unstructured free-text data within the Snowflake accounts of joint customers.
 Furthermore, Snowflake has collaborated with Kumo AI, a platform dedicated to AI/ML model development. They have launched Kumo as a Snowflake Native App in the Snowflake Marketplace as part of a private preview. Eligible customers can pay for the app with Snowflake Capacity commitment. It is developed using the Snowflake Native App Framework, offering data scientists the tools necessary to create predictive models.

</div> Analyst QuickTake: 
 Snowflake’s CEO announced that these collaborations are part of the company's strategic plan to stay competitive in the data and AI infrastructure space by enhancing its capabilities. The company is also actively seeking acquisitions and hiring new AI talent. Additionally, it released its own LLM, <a href="https: 
//sp-edge.com/updates/28792"> Arctic </a> , in April 2024, designed for generating database code.

Mindtech

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