Inspiration

Vikram Lander by ISRO(Really)

What it does

Detects Anomalies in autonomous systems and takes respective actions to recover the fault. Gives course correction by analyzing the temperature point of the Rocket and calculate the rectification and send to the system.

How I built it

1) Located the dataset that contains the temperature points of a rocket while on a trajectory. 2) Simulate the data using a model to represent the action and stores the data is Redis database. 3) Used the Redis Gears and Redis AI to predict the edges and anomalies in the temperature data to calculate the correct ignition and trajector course. 4) Calculate the correct course path based on the data and anomalies and mitigate the deviation.

Challenges I ran into

1) Installation and configuration of Redis modules 2) Redis AI installation on Windows 10 Home. 3) Configure Redis for AI model deployment.

Accomplishments that I'm proud of

What I learned

The best use cases of Redis and its module to build an efficient system architecture. Redis Gear, Redis AI, and Redis Stream

What's next for Xstream-ML

Deploy the actual model on Redis AI and refine the ML model for better edge detection. Deploy a model as a Graph on frontend UI for easier visualization in Redis Graph.

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