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
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
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
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/
Day 21: Scaling ML Model Inference with Kubernetes
🔗 https://www.learnxops.com/scaling-ml-model-inference-with-kubernetes/
Day 22: MLOps with ML Platforms (SageMaker & Vertex AI)
🔗 https://www.learnxops.com/mlops-with-ml-platforms-sagemaker-vertex-ai/
Day 23: Managing Large Language Models (LLMs) in Production
🔗 https://www.learnxops.com/mlops-for-llm-managing-large-language-models-in-production/
Day 24: Agentic AI & Retrieval‑Augmented Generation (RAG)
🔗 https://www.learnxops.com/agentic-ai-retrieval-augmented-generation-rag/
Day 25: Model Context Protocol (MCP) Explained for MLOps Engineers
🔗 https://www.learnxops.com/model-context-protocol-mcp-explained-for-mlops-engineers/
Day 26: Project: End-to-End MLOps Pipeline
🔗 https://www.learnxops.com/project-end-to-end-mlops-pipeline
Day 27: Model Deployment with Serverless Architectures
🔗 https://www.learnxops.com/model-deployment-with-serverless-architectures/
Day 28: Cost Optimization & Performance Tuning in MLOps
🔗 https://www.learnxops.com/cost-optimization-performance-tuning-in-mlops/
Day 29: Disaster Recovery & High Availability for ML Systems
🔗 https://www.learnxops.com/disaster-recovery-high-availability-for-ml-systems/
Day 30: MLOps Interview Question & Answers
🔗 https://www.learnxops.com/mlops-interview-question-answers/
Projects for LearnXops Premium Members
(Go Beyond Basics & Practical)
Project: LLMOps Pipeline - Build and Deploy Your Own AI Chat Agent