Mlops

Introduction TL;DR: NVIDIA Earth-2 is an open “weather AI stack” spanning data assimilation (HealDA), medium-range forecasting (Atlas), and nowcasting (StormScope), supported by Earth2Studio and PhysicsNeMo. NVIDIA Earth-2 appears designed to lower time-to-PoC for meteorology services and decision-heavy industries (insurance, energy) by shipping models and workflow tooling together. Why it matters: Weather AI adoption fails less on model quality and more on reproducibility, licensing, validation, and operational controls. What NVIDIA released in the Earth-2 family Three model lines: Atlas, StormScope, HealDA Earth-2 Medium Range (Atlas) targets global 15-day forecasts and 70+ variables. Earth-2 Nowcasting (StormScope) targets kilometer-scale severe weather prediction over 0–6 hours. Earth-2 Global Data Assimilation (HealDA) is positioned to generate initial conditions for forecasting workflows. Open tooling: Earth2Studio and PhysicsNeMo Earth2Studio is a Python package for building inference pipelines; docs warn that base installs may not cover all optional capabilities. PhysicsNeMo is positioned as the open framework for training/fine-tuning. Why it matters: Shipping the stack (not just a model) is what enables real integration into risk and operations pipelines. ...

Introduction TL;DR: Kubeflow is an ecosystem for running reproducible ML workflows on Kubernetes—from notebooks and pipelines to distributed training and model serving. (Kubeflow) In practice, “using Kubeflow” means wiring together Profiles/Namespaces, Notebooks, Pipelines (KFP), training (Trainer), tuning (Katib), and serving (KServe) with clear operational boundaries. (Kubeflow) 1) What “Kubeflow” is in 2026: Projects vs Platform Kubeflow can be installed as standalone projects (e.g., Pipelines-only) or as the integrated Kubeflow AI reference platform. The official “Installing Kubeflow” guide explicitly frames these as two installation methods. (Kubeflow) ...

Introduction TL;DR: Meta released Llama 4 Scout and Llama 4 Maverick on 2025-04-05. Scout targets ultra-long context (10M tokens) with 17B activated / 109B total params, while Maverick offers 1M tokens with 17B activated / 400B total params. Both are natively multimodal (text+image inputs) and use a Mixture-of-Experts (MoE) design. Benchmarks shared by Hugging Face show strong gains vs earlier Llama generations, but leaderboard integrity and “variant mismatch” issues mean you should validate on your own workloads. The “Llama 4 Community License” includes practical obligations and a major threshold clause (700M MAU) you must review before production use. In this post, we’ll focus on what’s verifiable from public artifacts (model cards, the license text, and release notes), then translate it into an engineer-friendly decision checklist. ...

Introduction TL;DR Before purchasing an enterprise GPU server, define your AI and data workloads in measurable terms and size GPU, chassis, storage, networking, power, and cooling accordingly. For always-on, high-utilization training or inference, on-premises GPU servers can become more cost-effective than cloud GPUs after roughly a year or more, depending on usage and pricing. Cloud and GPU hosting services excel for PoCs, bursty workloads, and smaller teams because they avoid upfront CapEx and enable rapid scaling. In practice, many enterprises adopt a hybrid model, keeping core, steady workloads on in-house GPUs and bursting to cloud when demand spikes. ...

AI Project Planning and Real-World Applications (Lecture 20) This is the final lecture of our 20-part series. We’ll conclude by discussing how to plan, design, and execute AI projects in real-world scenarios. You’ll learn about the AI project lifecycle, practical applications in various industries, and how to deploy models into production. Table of Contents {% toc %} 1) AI Project Lifecycle AI projects go beyond just training a model. They require a complete end-to-end strategy: ...

What is AutoML? Learn Automated Machine Learning with Python AutoML (Automated Machine Learning) refers to technologies that automate the entire machine learning pipeline, including data preprocessing, model selection, hyperparameter tuning, and evaluation. With AutoML, even beginners can build accurate ML models without deep technical expertise. Table of Contents Why AutoML? Imagine you’re running a coffee shop and want to predict which customers are likely to order an Americano. Building a machine learning model from scratch would require: ...