Synthetic Data Archives -

Mindtech Global

Danielle Perez — Sat, 19 Oct 2024 00:27:23 +0000

Mindtech Global is the developer of DataOps platforms for intelligently engineered synthetic data, enabling better AI models through data analysis, visualisation and curation. The company’s platforms – Chameleon and Dolphin empower rapid deployment of customer applications ranging across smart city.

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GenRocket

Danielle Perez — Sat, 19 Oct 2024 00:19:20 +0000

After years working as a principal engineer and chief architect in numerous high-profile software startups Hycel Taylor realized that each company was having the same problem when it came to software testing – limitations with test data. For over a decade, in his spare time, Hycel decided to think differently – rather than use production data as a source for test data – he developed, and patented, a high performance synthetic data engine that could accurately generate just about any data needed for developers, testers, AI / machine learning, data simulations and more.

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Hazy

Danielle Perez — Sat, 19 Oct 2024 00:08:33 +0000

Pioneers of synthetic data Hazy was the first company to take synthetic data to market as a viable enterprise product. With links to academics from the Alan Turing Institute and UCL, our research team continues to stay on the cutting edge of synthetic data.

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Datagen

Danielle Perez — Fri, 18 Oct 2024 23:59:31 +0000

Founded in 2015, Deepgram started with machine learning research for waveform analysis in a dark matter detector in China. CEO and co-founder Scott Stephenson and his teammate later explored deep learning for audio analysis at the University of Michigan. Seeing a gap in the speech-to-text market, they built Deepgram using end-to-end deep learning. Today, Deepgram offers advanced voice AI solutions beyond speech-to-text, including audio intelligence, text-to-speech, and a voice agent API, helping businesses worldwide enhance natural voice interactions. Their commitment to innovation continues to drive the future of voice technology.

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Tonic.ai

Danielle Perez — Fri, 18 Oct 2024 17:28:29 +0000

Tonic.ai was born out of a very tangible, practical need: equipping developers with high-quality, realistic data for dev and testing. Admittedly, it makes us sound like just another developer tool, but at the core of that need is our belief for why it truly matters. We wholeheartedly believe that data privacy is a human right. It isn’t just about complying with the latest regulation. It’s about helping organizations treat data the way we’d like our own data to be treated. Our team is uniquely equipped to solve this challenge, and in the long run, the solutions we’re building stand to benefit us all.

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Deep Vision Data

Danielle Perez — Wed, 09 Oct 2024 20:44:40 +0000

Deep Vision Data® specializes in the creation of synthetic training data for supervised and unsupervised training of machine learning systems such as deep neural networks, and also the use of digital twins as virtual ML development environments. Lack of machine learning datasets is often cited as the major development obstacle for deep learning systems, and creating and labeling sufficient data from physical testing and other non-algorithmic methods such as photography can be extremely time consuming or impossible. The problem is further compounded when the product or process being studied is under development and no physical data exists, or if the items of interest are rare and underrepresented in the physical dataset. Synthetic training data also mitigates privacy concerns associated with medical data and other private information. Learn more about synthetic data at Wikipedia. Our synthetic training data are created using a variety of proprietary methods, can be multi-class, and developed for both regression and classification problems. Data annotation is automatic, zero cost, and 100% accurate. Our machine learning datasets are provided using a database and labeling schema designed for your requirements. Contact us to discuss your particular machine learning data needs.

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Gretel.ai

Danielle Perez — Wed, 09 Oct 2024 20:40:29 +0000

We help developers build with data, together. We think the best ideas come from the blending of diverse perspectives. Our team is comprised of the best minds in their fields – and we believe that adding perspectives will make our solutions much stronger. We hire people who are superb at what they do, drawn to the cool edges where fields touch, and like to laugh. We are deeply collaborative, apolitical, and mission-oriented. Work from anywhere. The best and most creative thinkers are all over the planet, and our team has years of experience working in global teams.

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Rendered.Ai

Danielle Perez — Wed, 09 Oct 2024 20:32:30 +0000

Helping Customers Overcome Data Bias, Gaps, and Costs for AI/ML Training Rendered.ai was established after the realization that many industries were about to explode with massive investments in hardware-intensive imagery collection and analysis. Without the ability to access data during the design and development process, organizations are unable to validate analysis pipelines and business models before launching expensive hardware, sometimes literally, into the market. From space-based satellite imaging to manufacturing and security inspection, computer vision hardware and applications are proliferating across every industry. Relying on collected data alone carries risks and costs due to dataset biases and real data is simply not available for new sensors and platforms. Simulating sensor behavior and data output is a well-established technique used during the design and inception process for new equipment, but historically was not done at a scale sufficient to generate annotated data for the purpose of training computer vision algorithms. Rendered.ai was founded to connect simulation with data generation for computer vision and the team quickly demonstrated the potential for using simulated data to train Artificial Intelligence and Machine Learning systems with customers in the geospatial industry. Along the way, the team observed that simulated, or synthetic, data required an iterative....

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Mostly AI

Danielle Perez — Wed, 09 Oct 2024 20:25:32 +0000

MOSTLY AI was founded in 2017 in Vienna, Austria, by Michael Platzer, Klaudius Kalcher and Roland Boubela, three distinguished data scientists. They realized early on the potential of using AI to generate structured business data and to create what we now call synthetic data. Back then this was not much more than an idea. It was unclear how the process was going to work, since no previous research or competitors existed in the space. The inspiration came from the unstructured data domain where the first artificially created synthetic images were produced. The three co-founders experienced the challenges companies were facing with traditional data anonymization. These challenges only increased as GDPR was introduced in Europe in 2018. MOSTLY AI released the first version of its Synthetic Data Platform at the same time and proved to the world that synthetic data has a vast potential.

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Parallel Domain

Danielle Perez — Wed, 09 Oct 2024 20:16:18 +0000

Parallel Domain is a synthetic data generation platform for computer vision and autonomy. Parallel Domain provides a smart way to prepare both machines and human operators for the real world, while minimizing the time and miles spent. Users can connect to the Parallel Domain API and tap into the power of synthetic data to accelerate theirautonomous system development. Parallel Domain generates synthetic labeled data sets, simulation worlds, and controllable sensor feeds so users can develop, train, and test their algorithms safely before putting these systems into the real world.

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