When the Peloton Became a Dataset

1. 1Explore cloud platforms for data storage and processing
2. 2Build digital twins of cycling environments to simulate real-world conditions
3. 3Configure opponent models to predict competitor behavior
4. 4Apply machine learning algorithms to analyze cycling data
5. 5Test and evaluate the performance of ML models in cycling

Data scientists and machine learning engineers can benefit from understanding how ML is applied to professional cycling, while product managers can learn about the potential applications of ML in sports

💡 Machine learning can be applied to professional cycling to gain a competitive edge

🚴‍♂️ ML in cycling: cloud platforms, digital twins, and opponent models are changing the game 💻