The Future of AGI: Challenges and Opportunities

Explore the evolving landscape of artificial general intelligence — highlighting how existing systems excel at narrow tasks, while AGI aspires to human-like reasoning, adaptability, and cross-domain learning. AGI promises transformative breakthroughs in domains such as healthcare, scientific discovery, and personalized education, even as profound challenges remain in areas like real-time learning, ethical alignment, and safe deployment. (in progress)

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Ethical AI: Bias Detection

Understanding and mitigating bias in deep learning is fundamental to building fair, inclusive AI systems. Bias can manifest at different stages—unbalanced class distributions, misrepresentative training data, and flawed model assumptions. Common mitigation strategies include pre-processing (e.g., re-sampling or re-weighting data), in-processing (e.g., fairness-aware training and adversarial debiasing), and post-processing adjustments to outputs. Regular auditing, monitoring, and explainability are also vital for maintaining fairness over time, supported by tools like AIF360 and fairness dashboards. (in progress)

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Circassian DNA Chatbot

A hands-on guide to building a production-ready chatbot on the Circassian DNA project—illustrating how robust MLOps pipelines power each stage from training to deployment. Learn how we streamlined model management, versioning, and continuous integration for real-time, culturally-aware interactions. (in progress)

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Ultimate Throughput with vLLM

Discover how vLLM—powered by its virtual memory-inspired PagedAttention engine — significantly reduces memory fragmentation in KV caches, enabling near-zero waste and up to a 2-4 times throughput improvement over SOTA inference engines like FasterTransformer, especially with longer contexts and larger models, making it the new standard for high-performance LLM serving. vLLM inherently uses optimized CUDA-backed kernels—such as FlashAttention and FlashInfer — to achieve top-tier GPU utilization without requiring teams to write custom kernels. FlashInfer delivers substantial latency improvements for GPU-serving LLMs—including a 29-69% reduction in inter-token latency compared to Triton backends, 28-30% lower latency for long context inference, and a 13-17% performance boost in parallel generation scenarios. vLLM runs standard Hugging Face models, integrates seamlessly with PyTorch, and supports OpenAI-compatible APIs — so you don't need to rebuild; just swap in a better engine. Hugging Face integration confirms that vLLM can run standard Hugging Face models without rewriting your workflows. PyTorch ecosystem integration shows it fits seamlessly into the PyTorch ecosystem and supports diverse hardware backends with minimal hassle. And a built-in OpenAI-compatible server demonstrates vLLM's ability to mimic the OpenAI API, enabling plug-and-play usage in existing setups. In less than a year, vLLM accelerated from 30K to 50K stars on GitHub — highlighting its fast-moving momentum, robust open-source growth, and deep support from the developer community.

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KServe Meets vLLM: Cloud-Native Scalable LLM Serving on K8s

KServe is a Kubernetes-native model serving platform offering enterprise-grade capabilities for predictive and generative AI—including autoscaling, ModelMesh routing, and standardized APIs across Hugging Face and vLLM models. Since the v0.13 release, KServe officially supports vLLM as a backend runtime, enabling high-performance LLM serving within the Kubernetes ecosystem. With the v0.15 release, KServe added advanced LLM-specific features including distributed KV caching via LMCache, LLM-aware autoscaling using KEDA, and integration with the Envoy AI Gateway for intelligent routing and traffic management. KServe supports a plug-and-play architecture using custom ServingRuntimes and InferenceService CRDs, enabling optimized LLM serving via vLLM backends across GPUs (NVIDIA, AMD, Intel) and CPUs. Seamless integration matters - leverage vLLM's high-efficiency engine without rebuilding your stack while KServe handles orchestration, scaling, and routing. Features like LMCache maximize GPU throughput critical for LLMs. Offloading of KV-cache layers reduces inference costs and helps meet SLOs for latency and throughput. (in progress)

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Kubeflow: Cloud-Native MLOps on K8s for Scalable ML Workflows

Kubeflow is a modular, open-source MLOps platform built on Kubernetes, designed to orchestrate the entire machine learning lifecycle — from notebooks and pipeline orchestration to distributed training, hyperparameter tuning, and model serving via KServe. Its modular architecture allows you to adopt only the components you need. Kubeflow supports Notebooks, Pipelines, Training Operators, Katib, and KServe — covering all stages from experimentation to deployment. Kubeflow Pipelines enables building portable, container-based workflows deployable across any Kubernetes environment. As a CNCF-incubated project backed by Google, Red Hat, IBM, NVIDIA, and others, Kubeflow delivers enterprise-grade scalability, hybrid-cloud portability, and robust governance. For teams with Kubernetes-first infrastructure, Kubeflow offers a production-ready foundation for reproducible, scalable ML workflows — without stitching together disparate tools. Moreover, Kubeflow's tight integration with Kubernetes enables efficient resource management—especially for GPU workloads—making it ideal for large-scale ML operations. For ML teams focused mainly on experiment tracking, model versioning, and ease-of-use, MLflow is a great MLOps solution.

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