Ray

Prepare the training data

Distribute the data across the Ray cluster using Ray Data

Fine-tune the LLM using Ray Train and your framework of choice

Monitor training progress using Ray's debugging tools

Deploy the fine-tuned model using Ray Serve

Ingest transaction data from various sources

Preprocess the data using Ray Data

Deploy a fraud detection model using Ray Serve

Scale the serving infrastructure to handle peak transaction volumes

Monitor model performance and retrain as needed

Create a simulation environment using a game engine or custom code

Run multiple simulations in parallel using Ray

Collect data from the simulations and use it to train reinforcement learning models

Evaluate the performance of the trained models in the simulation environment

Deploy the models to real-world vehicles

Define the environment and the agents

Implement the reward function and observation space

Use Ray RLlib to train the agents

Evaluate the performance of the trained agents

Tune hyperparameters to improve performance

Load the images using Ray Data

Distribute the images across the Ray cluster

Run inference on the images using a pre-trained model

Collect the results and store them in a database

Analyze the results and generate reports