Inference Basics and Why Model Serving Is Hard

1. 1Read the article on Medium to understand the basics of inference
2. 2Explore the challenges of model serving in machine learning
3. 3Research different model serving strategies and tools
4. 4Build a simple model serving pipeline using a framework like TensorFlow or PyTorch
5. 5Test and evaluate the performance of the model serving pipeline

Data scientists and machine learning engineers can benefit from understanding the fundamentals of inference and model serving to improve their workflow and deployment efficiency

💡 Model serving is a critical step in the machine learning workflow, but it can be challenging due to issues like scalability, security, and compatibility

🤖 Learn the basics of inference and model serving in ML! 📊