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Machine Learning FundamentalsLearn the fundamentals of supervised learning by implementing linear regression from scratch without libraries. Understand gradient descent, loss functions, and the mathematics behind ML.
AI Editorial TeamMachine Learning FundamentalsMove beyond theory to practical feature engineering techniques that improve model performance in real-world applications. Learn which methods deliver ROI and which are time-wasters.
Sarah ChenMachine Learning FundamentalsAccuracy is misleading when classes are imbalanced. Learn advanced evaluation techniques that reveal true model performance for fraud detection, disease diagnosis, and rare event prediction.
Dr. Aisha PatelCloud Infrastructure for AI/MLBeginner-friendly comparison of the three major cloud ML platforms. Learn which service fits your needs based on pricing, features, ease of use, and integration with your existing stack.
AI Editorial TeamCloud Infrastructure for AI/MLCut your AI training costs by 70%+ with proven strategies for GPU instance optimization. Learn how to use spot instances, implement auto-scaling, and manage budgets without sacrificing performance.
Marcus JohnsonCloud Infrastructure for AI/MLAdvanced security implementation for AI systems in the cloud. Learn zero-trust principles, model protection, data encryption throughout ML pipeline, and compliance automation for regulated industries.
Dr. Aisha PatelTools, Frameworks & LibrariesA comprehensive comparison of TensorFlow and PyTorch for newcomers to machine learning, with practical guidance on which framework to learn first based on your goals.
AI Editorial TeamTools, Frameworks & LibrariesA comprehensive guide to deploying ML models at scale using BentoML, covering containerization, versioning, auto-scaling, and monitoring for production workloads.
Sarah ChenTools, Frameworks & LibrariesDeep dive into optimizing transformer model inference performance using ONNX Runtime and NVIDIA TensorRT, with comprehensive benchmarks across hardware platforms.
Marcus JohnsonMLOps & Production DeploymentLearn how to build your first machine learning pipeline using Kubeflow, containerize components, and orchestrate training-to-deployment workflows on Kubernetes.
Marcus JohnsonMLOps & Production DeploymentImplement comprehensive monitoring for production ML models with drift detection, performance tracking, and automated retraining triggers using open-source tools.
Sarah ChenMLOps & Production DeploymentImplement GitOps workflows for ML models with automated testing, canary deployments, rollback strategies, and multi-environment promotion using ArgoCD and MLflow.
AI Editorial TeamAI Research & Emerging TechnologiesA beginner-friendly guide to transformer architecture, attention mechanisms, and how large language models process text.
AI Editorial TeamAI Research & Emerging TechnologiesStep-by-step implementation of Retrieval-Augmented Generation with real code, vector database setup, and production considerations.
Sarah ChenAI Research & Emerging TechnologiesAdvanced techniques for production LLM serving: quantization methods, dynamic batching, KV caching, and hardware-aware optimization.
Marcus JohnsonEthics, Governance & Responsible AILearn the essential ethical principles, frameworks, and practical tools every ML engineer must master in 2026 to build responsible AI systems that are fair, transparent, and trustworthy.
Dr. Aisha Patelcloud-security-ai-systemsComprehensive security guide for ML systems covering data protection, model security, infrastructure hardening, and compliance across the entire ML lifecycle in cloud environments.
Sarah ChenEthics, Governance & Responsible AIPractical implementation guide for the EU AI Act compliance: risk classification, technical documentation, conformity assessments, and deployment requirements for high-risk AI systems.
Dr. Aisha Patel