Tesla Dojo and Palantir Edge AI?
What is Tesla Dojo?
Dojo is Tesla’s own custom supercomputer platform built from the ground up for AI machine learning and, more specifically, for video training using the video data coming from its fleet of vehicles.
The custom-built supercomputer is expected to elevate Tesla’s capacity to train neural nets using video data, which is critical to the computer vision technology powering its self-driving effort.
Whitepaper on Palantir's Edge AI
Palantir's AI orchestration and sensor fusion engine that enables autonomous decision-making across edge devices and environments. Designed for situations where time and efficiency matter.
KEY BENEFITS OF PALANTIR EDGE AI
Speed - Accelerate operations with autonomous decision-making and by connecting devices across environments
Scale - Manage thousands of models at scale — federate development, training, and evaluation to internal teams and third-party vendors
Security - Lower risk and reduce single points of failure by distributing analysis and decision-making to the edge
For Sensor Manufacturers - Embed AI/ML alongside your sensors, enabling your customers to train, manage, and deploy models across the fleet of sensors
Palantir integrates data of any type or complexity — like high-volume sensor and streaming video data — then performs critical tasks around data quality, provenance, and cleaning to render a usable data asset for models. Complete AI/ML infrastructure offers the ability to independently version, test, release, and deploy models.
The biggest challenge in AI/ML today is defining valuable, solvable problems and then continuously deploying models against them. Palantir Edge AI addresses this challenge with a new technology called Micro Models. Micro Models are modular, operation-specific models designed around a measurable objective. They can be homegrown, open-source, or third-party algorithms.
Palantir Edge AI runs on an organization’s specialized compute hardware or other low-SWaP form-factors. Deploying at the point of use enables optimum quality AI detections derived from the highest quality sensor, IoT, and video inputs.
Algorithms in Palantir Edge AI can be updated and deployed on live feeds with little to no downtime, tightening the feedback loop in model retraining. Teams can optimize models on factors including quality of output, speed, and bandwidth.
Palantir integrates real-time sensor data from Palantir Edge AI with an enterprise’s existing data foundation, giving teams a valuable corpus of historical data. This historical data can be used to simulate edge environments in Palantir and retrain models accordingly. Palantir Edge AI then extends your simulated environment into the physical world, where models autonomously run on sensors in a lower latency, offline, low bandwidth environment.
When deploying computer vision models, Palantir Edge AI simplifies video preprocessing, allowing teams to focus on the models. The platform supports a range of cameras and sensors, including various video protocols and codecs. Specifically, Palantir Edge AI handles all video and metadata decoding and produces individual frames of imagery data and metadata for models to correct and analyze in order to augment live feeds. |
