MLOps - Setup a Home Machine Learning Lab
Transitioning from DevOps to MLOps can be achieved by leveraging existing DevOps expertise by adding new layers specific to machine learning. Key Differences: Model Lifecycle Management: MLOps handles model training, deployment, and retraining. Data Versioning: Tools like DVC ensure dataset version control. Experiment Tracking: MLflow and Weights & Biases track model training parameters and results. Model Serving: Deploy models with TensorFlow Serving or TorchServe. Model Drift: Monitor data changes over time to trigger retraining. ...