MLOps & Infrastructure

Effective MLOps: Model Development

Weights & Biases' free course on building production-ready ML pipelines — covering experiment tracking, model versioning, hyperparameter sweeps, and CI/CD for ML. Practical and tool-agnostic; relevant for any team running ML in production.

Machine Learning Engineering for Production (MLOps) Specialization

DeepLearning.AI's four-course MLOps specialization covering the ML production lifecycle: data management, model pipelines, deployment, and monitoring. The standard reference for engineering teams building maintainable AI systems in regulated environments.

MLOps | Machine Learning Operations Specialization

Duke University's MLOps specialization covering containerization, CI/CD for ML, Kubernetes-based deployment, and monitoring in production. Emphasizes reproducibility and auditability — critical requirements for AI systems in regulated industries.

Building Machine Learning Pipelines

A practical guide to automating the ML lifecycle with TensorFlow Extended — covering data validation, preprocessing, model training, analysis, and deployment pipelines. Directly applicable to teams building auditable, reproducible ML systems.

Machine Learning Production Systems

A systems engineering approach to ML in production — covering data pipelines, model serving architectures, A/B testing, monitoring, and the organizational structures that support reliable ML operations in regulated enterprise environments.

Engineering MLOps

A practical guide to building MLOps workflows at enterprise scale — covering model versioning, CI/CD for ML, monitoring, and governance. Addresses the specific auditability and reproducibility requirements that regulated industries impose on AI systems.

MLOps Community Podcast and Talks

The MLOps Community's library of practitioner talks — covering model monitoring, feature stores, deployment strategies, and production failure post-mortems. Grounded in real-world experience rather than academic theory; valuable for any MLOps team.

Chip Huyen: Designing ML Systems for Production

Chip Huyen's lecture on real-world ML systems design — covering data distribution shifts, monitoring strategies, and the organizational challenges of maintaining ML in production. Directly applicable to teams managing AI systems in regulated environments.

MLOps: From Model-Centric to Data-Centric AI

Andrew Ng's talk on shifting from model-centric to data-centric AI — arguing that consistent, high-quality training data matters more than model architecture improvements. A paradigm shift for ML teams focused on production performance in regulated environments.

How Deutsche Telekom is rewiring telecommunications with AI

Secure Decentralized Federated Learning via Gossip and Virtual Voting

Building the foundation for an autonomous enterprise

Trust-free Personalized Decentralized Learning

Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks

Weights & Biases

The leading ML experiment tracking and collaboration platform — covering experiment logging, visualization, hyperparameter sweeps, and model versioning. Widely used by enterprise ML teams who need reproducible experiments and collaborative model development.

Evidently AI

An open-source ML observability framework providing 100+ metrics for data drift detection, model performance monitoring, and evaluation. Essential for regulated-industry teams that must demonstrate ongoing model validity to auditors and regulators.

Kubeflow

Google's open-source ML platform for Kubernetes — covering end-to-end ML pipeline orchestration, model training, hyperparameter tuning, and serving. Designed for teams deploying ML at enterprise scale with full Kubernetes integration and portability.