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πŸ“… 30 Days Blog Challenge Tracker

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β€’3 min read
πŸ“… 30 Days Blog Challenge Tracker
B
Bittu Sharma

I am Bittu Sharma, a DevOps & AI Engineer with a keen interest in building intelligent, automated systems. My goal is to bridge the gap between software engineering and data science, ensuring scalable deployments and efficient model operations in production.! π—Ÿπ—²π˜'π˜€ π—–π—Όπ—»π—»π—²π—°π˜ I would love the opportunity to connect and contribute. Feel free to DM me on LinkedIn itself or reach out to me at bittush9534@gmail.com. I look forward to connecting and networking with people in this exciting Tech World.

Day Blog Title Link
Day 1 ML Lifecycle https://bittublog.hashnode.dev/30-days-of-mlops-challenge-day-1-understanding-mlops-and-its-role-in-the-ml-lifecycle
Day 2 Role in the ML Lifecycle https://bittublog.hashnode.dev/30-days-of-mlops-challenge-day-1-understanding-mlops-and-its-role-in-the-ml-lifecycle
Day 3 Data Versioning with DVC https://bittublog.hashnode.dev/day-3-of-30-day-mlops-challenge-mastering-data-versioning-with-dvc
Day 4 ML Environments using Conda & Docker https://bittublog.hashnode.dev/day-4-of-30-day-mlops-challenge-reproducible-ml-environments-using-conda-and-docker
Day 5 Feature Engineering and Feature Stores https://bittublog.hashnode.dev/day-5-of-30-day-mlops-challenge-feature-engineering-and-feature-stores
Day 6 ML Models with Scikit-learn & TensorFlow – Build & Save Your Models Like a Pro https://bittublog.hashnode.dev/day-6-of-30-day-mlops-challenge-training-ml-models-with-scikit-learn-and-tensorflow-build-and-save-your-models-like-a-pro
Day 7 Model Experiment Tracking with MLflow https://bittublog.hashnode.dev/day-7-of-30-day-mlops-challenge-content-on-model-experiment-tracking-with-mlflow-log-it-or-lose-it
Day 8 Model Evaluation & Metrics https://bittublog.hashnode.dev/day-8-of-30-days-of-mlops-challenge-model-evaluation-and-metrics
Day 9 Automate & Orchestrate ML Workflows https://bittublog.hashnode.dev/day-9-ml-pipelines-with-kubeflow-pipelines-automate-and-orchestrate-ml-workflows
Day 10 Serving ML Models with FastAPI & Flask https://bittublog.hashnode.dev/day-10-of-30-days-of-mlops-challenge-serving-ml-models-with-fastapi-and-flask
Day 11 Docker – Containerize & Deploy Your ML Models https://bittublog.hashnode.dev/day-11-of-30-days-of-mlops-challenge-packaging-models-with-docker-containerize-and-deploy-your-ml-models
Day 12 CI/CD for ML with GitHub Actions – Automate Test-Train-Deploy Pipelines https://bittublog.hashnode.dev/day-12-of-30-days-of-mlops-challenge-cicd-for-ml-with-github-actions-automate-test-train-deploy-pipelines
Day 13 ML Model Deployment – Batch vs Real-time Inference https://bittublog.hashnode.dev/day-13-of-30-days-of-mlops-challenge-ml-model-deployment-batch-vs-real-time-inference
Day 14 Data Drift & ML Model Drift Detection – Keep Your Models Relevant https://bittublog.hashnode.dev/day-14-of-30-days-of-mlops-challenge-data-drift-and-ml-model-drift-detection-keep-your-models-relevant
Day 15 Automated Retraining ML Pipelines To Keep Your ML Models Fresh https://bittublog.hashnode.dev/30-days-of-mlops-challenge-day-15-automated-retraining-ml-pipelines-to-keep-your-ml-models-fresh
Day 16 Ensuring Security for Machine Learning Systems https://bittublog.hashnode.dev/day-16-in-30-day-mlops-series-ensuring-security-for-machine-learning-systems
Day 17 Understanding ML Model Governance: https://bittublog.hashnode.dev/understanding-ml-model-governance-day-17-of-the-30-day-mlops-challenge
Day 18 Explainable AI (XAI) in Production – SHAP, LIME, and Interpretability Techniques https://bittublog.hashnode.dev/30-days-of-mlops-challenge-day-18-explainable-ai-xai-in-production-shap-lime-and-interpretability-techniques
Day 19 Monitoring ML Systems in Production https://bittublog.hashnode.dev/day-19-monitoring-ml-systems-in-production-30-days-of-mlops-challenge
Day 20 Model Registry: Managing and Versioning ML Models https://bittublog.hashnode.dev/day-20-model-registry-managing-and-versioning-ml-models-30-days-of-mlops-challenge
Day 21 Scaling ML Model Inference with Kubernetes https://bittublog.hashnode.dev/day-21-scaling-ml-model-inference-with-kubernetes-30-days-of-mlops-challenge
Day 22 Streamlining MLOps Pipelines with SageMaker and Vertex AI https://bittublog.hashnode.dev/day-22-streamlining-mlops-pipelines-with-sagemaker-and-vertex-ai
Day 23 Scaling & Managing LLMs in Production Environments https://bittublog.hashnode.dev/day-23-scaling-and-managing-llms-in-production-environments
Day 24 Agentic AI and RAG Workflows https://bittublog.hashnode.dev/agentic-ai-and-rag-workflows-30-days-of-mlops-day-24
Day 25 n Introduction to Model Context Protocol (MCP) for MLOps https://bittublog.hashnode.dev/day-25-in-30-days-an-introduction-to-model-context-protocol-mcp-for-mlops
Day 26 Build an End-to-End MLOps Pipeline https://bittublog.hashnode.dev/build-an-end-to-end-mlops-pipeline-in-30-days-day-26
Day 27 Model Deployment: Serverless Solutions https://bittublog.hashnode.dev/30-day-guide-to-model-deployment-day-27-serverless-solutions
Day 28 Cost and Performance Enhancements https://bittublog.hashnode.dev/day-28-of-mlops-challenge-cost-and-performance-enhancements
Day 29 Disaster Recovery and High Availability in MLOps https://bittublog.hashnode.dev/day-29-challenge-key-lessons-on-disaster-recovery-and-high-availability-in-mlops
Day 30 MLOps Challenge: Essential Interview Q&A https://bittublog.hashnode.dev/complete-day-30-of-mlops-challenge-essential-interview-qanda
#mlops#devops#ai

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