RenderedAI

Rendered.ai participated in a panel discussion at GTC22, with NVIDIA and Quantiphi, titled “Accelerating AI Development with GPU-optimized Software”
Rendered.ai participated in a panel discussion at GTC22, with NVIDIA and Quantiphi, titled “Accelerating AI Development with GPU-optimized Software”

Watch this on-demand session from spring GTC22 where our synthetic data engineer, Matt Robinson, and experts from NVIDIA and Quantiphi discuss how GPU-optimized software can accelerate AI development and help enterprises gain a competitive edge by delivering better services and improving operations.

👉Check out the recording

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Synthetic images generated using the Rendered.ai PaaS for synthetic data to train AI and ML models.
Synthetic images created using the Rendered.ai PaaS for generating synthetic data to train AI and ML.

Curious about what Synthetic Data is? Listen to our CEO Nathan Kundtz on how it can change the way we build AI models!

Introduction to Synthetic Data by Nathan Kundtz, CEO of Rendered.ai

Interested in generating your very own synthetic dataset? Check out the example channel in our PaaS for synthetic data. Get Started!

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CEO of Rendered.ai, Nathan Kundtz, featured on NVIDIA AI’s Podcast to discuss the growing role of Synthetic Data
CEO of Rendered.ai, Nathan Kundtz, featured on NVIDIA AI’s Podcast to discuss the growing role of Synthetic Data

Data is the fuel that makes artificial intelligence run, but compiling quality real-world data for #AI and #ML can be difficult and expensive. Fortunately, Synthetic Data solves this problem.

Check out NVIDIA AI’s latest podcast episode featuring our CEO, Nathan Kundtz, to learn about the growing role of synthetic data in AI!

👉Listen to the full episode

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Are you curious about how to increase your ability to innovate, reduce costs of #data acquisition, and reduce the impact of real data collection on the environment? Check out the video recording of our webinar, An Introduction to Synthetic Data and the Rendered.ai Platform featuring Heera Raj, Dan Hedges, and Tom Freeman.

The webinar covers what synthetic data is, how to use synthetic data to reduce expenses, close gaps, and overcome bias, security, privacy issues, and how the Rendered.ai Platform can help you create and deploy synthetic data by providing a configuration environment, samples, and cloud resources to quickly get started.

Want to check out our PaaS for Synthetic Data? Get started

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The Rendered.ai team attended CVPR 2022. Check out the video tutorial on how to use the Rendered.ai PaaS for generating physics-based synthetic data for AI and ML algorithms.

Missed our tutorial at CVPR2022? Check out the home office version!

The Rendered.ai platform can be used to create and deploy channels for unlimited synthetic data generation, how to extend your synthetic data workflows with post-processing GAN domain adaptation, UMAP comparison between datasets, and more!

👉Find the slides and video here: https://hubs.li/Q01h8R1T0

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Rendered.ai exhibited at the Esri user conference. Check out Rendered.ai to learn more about how synthetic data and a platform to support synthetic data can help meet your data needs!
Rendered.ai exhibited at the Esri User Conference this past week. Pictured: COO, Chris Andrews, at the Rendered.ai booth during the Esri UC.

We’d like to thank everyone who joined the Rendered.ai team at the Esri UC for a fantastic week. It was a terrific opportunity to interact with the broad GIS community!

It’s exciting to see the rise in AI and ML across so many GIS applications. We loved sharing how ArcGIS content and services can be used as synthetic environments to simulate training data for AI. We are thrilled that our platform to support synthetic data continues to resonate with many so of you!

Missed us? Try out our PaaS for Synthetic Data for a demo! Get started: https://hubs.li/Q01bYXpy0

👉Read about our partnership with Esri: https://hubs.li/Q01dCxD40

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Article on how businesses are synthesizing their own data. Rendered.ai is a platform for generating synthetic data to train AI.
Businesses are synthesizing their own data because of the cost and difficulty of using real data. To use synthetic data as an enterprise capability, companies will need the Rendered.ai platform.

The cost and difficulty to acquire and use real sensor data for computer vision is an industry-wide issue. Luckily, synthetic data is an emerging solution to this problem. Synthetic Data is engineered data designed for a specific purpose with characteristics of real-world data.

Check out this great article by AI Partnerships featuring our COO, Chris Andrews, that details how and why businesses are synthesizing their own data!

👉Read it here: https://hubs.li/Q01gPvhh0

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If you’re attending the Esri User Conference, EsriUC, this upcoming week make sure you stop by the Rendered.ai booth, Z19 located in the startup zone, to learn more about how ArcGIS content and services can be used as synthetic environments to simulate training data for AI!

Don’t forget to join our COO, Chris Andrews, at his talk on July 13th from 3:00–3:20 pm on “Using ArcGIS Raster Services for Synthetic Training Data with Rendered.ai.”

👉Read about our partnership with Esri: https://hubs.li/Q01dCxD40

👉Esri Startup Zone Blog: https://hubs.li/Q01gBV_p0

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Sam Kulkarni will be on a panel discussing her path to becoming a software engineer in the AI industry alongside other industry experts!

Join our software engineer, Sam Kulkarni, on a panel event discussing diverse pathways to enter the AI industry hosted by Women in AI!

The event, “Career Launchpad — Diverse AI Pathways,” is a part of the #WaiENGAGE series which is a fantastic way for women and diverse communities across AI, ML, and data science to connect and promote ethical innovation.

Join the event “Career Launchpad — Diverse AI Pathways”:

📅 When: July 07, 2022 at 5:00 PM EDT

⏳ Register for free: https://hubs.li/Q01gdvTY0

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RenderedAI

Rendered.ai is a Platform as a Service for data scientists, engineers, and developers who need to create and deploy unlimited, customized synthetic data pipelines for #ML and #AI workflows, reducing expense, closing gaps, and overcoming bias compared with using real-world data.

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