ai-ml-pro

Building end-to-end machine learning workflows requires tools for data preprocessing, model training, evaluation, and deployment. This Skill provides scripts to create training pipelines, model serving endpoints, and experiment tracking systems.

Monitoring production ML systems for data drift requires implementing versioning, A/B testing, and drift detection mechanisms. This Skill supports model versioning and continuous monitoring for both data drift and concept drift in deployed systems.

Yes, you can use TensorFlow and PyTorch models within the same ML pipeline. This Skill supports multiple frameworks including scikit-learn, TensorFlow, and PyTorch for model training and deployment.

To start training an ML model, run the 'train_model' script within the 'src/pipelines/' directory using the provided 'experiment_001.yaml' configuration file. This initiates the model training pipeline.

Yes, MLflow works with this Skill for experiment tracking. MLflow is a core dependency, enabling you to track experiments, manage model versions, and facilitate the creation of tracking systems.

To set up MLOps pipelines for image recognition models, you need Python for execution and frameworks like TensorFlow or PyTorch. This Skill guides you from setting up data pipelines to deploying the model with monitoring and version control.