Day 1: Intro to MLOps – ML Meets DevOps

🔗 https://www.learnxops.com/intro-to-mlops-ml-meets-devops/

Day 2: MLOps Tools Landscape – Explore the Ecosystem
🔗 https://www.learnxops.com/mlops-tools-landscape-explore-the-ecosystem/

Day 3: Data Versioning with DVC – Reproducible ML Starts with Data

🔗 https://www.learnxops.com/data-versioning-with-dvc-reproducible-ml-starts-with-data/


Day 4: Reproducible ML environments using Conda & Docker

🔗 https://www.learnxops.com/reproducible-ml-environments-using-conda-docker/


Day 5: Feature Engineering & Feature Stores – Fueling ML with Quality Features

🔗 https://www.learnxops.com/feature-engineering-feature-stores-fueling-ml-with-quality-features/

Day 6: Training ML Models with Scikit-learn & TensorFlow – Build & Save Your Models Like a Pro

🔗 https://www.learnxops.com/training-ml-models-with-scikit-learn-tensorflow-build-save-your-models-like-a-pro/

Day 7: Model Experiment Tracking with MLflow – Log It or Lose It

🔗 https://www.learnxops.com/model-experiment-tracking-with-mlflow-log-it-or-lose-it/

Day 8: Model Evaluation & Metrics – Measure What Matters

🔗 https://www.learnxops.com/model-evaluation-metrics-measure-what-matters/

Day 9: ML Pipelines with Kubeflow Pipelines - Automate & Orchestrate ML Workflows

🔗 https://www.learnxops.com/ml-pipelines-with-kubeflow-pipelines-automate-orchestrate-ml-workflows/

Day 10: Serving ML Models with FastAPI & Flask

🔗 https://www.learnxops.com/serving-ml-models-with-fastapi-flask/

Day 11: Packaging Models with Docker – Containerize & Deploy Your ML Models

🔗 https://www.learnxops.com/packaging-models-with-docker-containerize-deploy-your-ml-models/

Day 12: CI/CD for ML with GitHub Actions – Automate Test-Train-Deploy Pipelines

🔗 https://www.learnxops.com/packaging-models-with-docker-containerize-deploy-your-ml-models-in-production/

Day 13: ML Model Deployment – Batch vs Real-time Inference

🔗 https://www.learnxops.com/ml-model-deployment-batch-vs-real-time-inference/

Day 14: Data Drift & ML Model Drift Detection – Keep Your Models Relevant

🔗 https://www.learnxops.com/data-drift-ml-model-drift-detection-keep-your-models-relevant/



Day 15: Automated Retraining ML Pipelines To Keep Your ML Models Fresh

🔗 https://www.learnxops.com/automated-retraining-ml-pipelines-to-keep-your-ml-models-fresh/


Day 16: Security in MLOps – Protecting ML Systems at Every Layer

🔗 https://www.learnxops.com/security-in-mlops-protecting-ml-systems-at-every-layer/

Day 17: Explainable AI (XAI) in Production – SHAP, LIME, and Interpretability Techniques

🔗 https://www.learnxops.com/explainable-ai-xai-in-production-shap-lime-and-interpretability-techniques/

Day 18: ML Model Governance & Compliance – Auditing, Explainability & Fairness in ML

🔗 https://www.learnxops.com/ml-model-governance-compliance-auditing-explainability-fairness-in-ml/

Day 19: Monitoring ML Systems in Production – Metrics, Logging, Alerting

🔗 https://www.learnxops.com/monitoring-ml-systems-in-production-metrics-logging-alerting/


Day 20: Model Registry – Managing and Versioning ML Models

🔗 https://www.learnxops.com/model-registry-managing-and-versioning-ml-models/