TDJ Pitango invests using the smart money formula and supports the best founders in scaling their startups globally. The fund concentrates on early and growth phases, investing $1-5m per round into startups with strong technology, operating on large or quickly growing markets. Drawing on over 25 years of successful VC investment experience, TDJ Pitango employs deep knowledge and understanding of what it takes for an organization to become a truly global company and how to build impressive value. The fund actively supports its portfolio startups in the value-building journey by providing them with access to networks and by attracting the best experienced senior talent to the startups. The fund helps in fundraising for the next financing rounds and actively supports the exiting process. TDJ Pitango also brings expertise related to strategy and business development, HR and growing pains challenges as well as legal and finance. The fund is Founders-friendly, invests in minority stakes, and very much likes to co-invest with other, smart investors, who bring value to the table.

TDJ Pitango Ventures

B2venture is a venture capital firm focused on early-stage investments in digital and industrial technology companies. B2venture invests EUR 100m+ per year in high-growth companies with ticket sizes from EUR 0.25m to 3.5m.

b2venture

Rheingau Founders is a Berlin&Munich based Venture Capital firm active since 2011. Focusing on pre-seed to Series A investments in Europe, predominantly B2B in the fields of Marketplaces, Software as a Service, InsurTech, and eHealth. Rheingau targets start-ups that have the potential to sustainably disrupt existing processes and value chains. Besides the financial investment, we offer strategic support through the mentorship of our partners.

Rheingau Founders

Almaz Capital is an international venture capital firm investing in early stage, capital efficient technology companies. A unique bridge fund headquartered in Silicon Valley, Almaz Capital connects Eastern European/former Soviet engineering and entrepreneurial talent to the U.S. market and helps build global businesses. The firm invests in high-growth sectors such as artificial intelligence and blockchain applications, data engineering, cybersecurity, industrial IoT, networking architecture and infrastructure. Its investors include Cisco, the European Bank for Reconstruction and Development (EBRD), and International Finance Corporation (IFC), a member of the World Bank Group.  Founded in 2008, Almaz Capital has invested in over 30 portfolio companies and has had nine exits to date.

Almaz Capital

neptune.ai

Neptune is an experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover. It offers a single place to track, compare, store, and collaborate on experiments so that Data Scientists can develop production-ready models faster and ML Engineers can access model artifacts instantly in order to deploy them to production. Unlike manual, homegrown, or open-source solutions, Neptune is a scalable, full-fledged component with user access management, developer-friendly UX, and collaboration features. Unlike end-to-end MLOps platforms, Neptune is a point solution that gives you only the capabilities you need from an experiment tracker and plugs into every stack.

Neptune.ai allows users to manage model metadata via a dedicated metadata store that logs, stores, displays, organizes, shares, compares, and queries all metadata generated during an ML model lifecycle.. The platform monitors learning curves, trained model predictions distribution, and confusion matrices during model training and re-training. The platform provides a centralized model repository to store production-ready models and metadata and allows users to review models and transition across development stages. Neptune focuses on ML verticals, such as computer vision, time-series forecasting, natural language processing, and reinforcement learning.

The toolkit to build machine learning applications at scale

Machine Learning Infrastructure

Neptune.ai

neptune.ai on LinkedIn: #llm #mlops #ai

Neptune.ai allows users to manage model metadata via a dedicated metadata store that logs, stores, displays, organizes, shares, compares, and queries all metadata generated during an ML model lifecycle.. The platform monitors learning curves, trained model predictions distribution, and confusion matrices during model training and re-training. The platform provides a centralized model repository to store production-ready models and metadata and allows users to review models and transition across development stages. Neptune focuses on ML verticals, such as computer vision, time-series forecasting, natural language processing, and reinforcement learning.Neptune.ai provides its platform under a SaaS subscription mode, which includes a free plan with limited features.Key customers and partnershipsThe company served more than 50,000 data scientists and ML engineers and over 1,500 commercial and research teams as of January 2024. Its notable customers include Roche, Wefox, Continuum, Brainly, InstaDeep, Samsung, and Senseye.In September 2023, Neptune.ai partnered with Modelbit to offer an integration that enables better ML model deployment and experiment tracking. Developers can use the integration to train and deploy machine learning models in Modelbit while logging and visualizing training progress in Neptune.Funding and financialsIn April 2022, the company raised USD 8 million in a Series A funding round led by Almaz Capital. The funds were utilized to grow its product and expand its engineering teams.The company’s reported revenue increased by 4x and its platform usage increased 8x over 2021–2022.

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

Neptune.ai

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