Tag: MLOps
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safetensors and Model File Formats: Why the Format Is a Security Decision (Hugging Face Series, Part 7)
A PyTorch .bin checkpoint is a pickle, and loading one can run code on your host. Here is why safetensors fixes that by design, how to convert and load safely, and where to scan models before they reach your GPU hosts.
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The Hugging Face datasets Library: Loading, Streaming, and Disk Survival (Hugging Face Series, Part 6)
The datasets library is a data-movement and storage problem before it is a data-science one. Here is how loading, streaming, and the Arrow cache actually behave, and how to keep them from filling your disk.
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Hugging Face Access Tokens and the hf CLI: Credentials Done Right (Hugging Face Series, Part 3)
A Hugging Face access token is a credential, not a convenience. Here is how to scope, store, rotate and use tokens with the new hf CLI, written for the infrastructure engineer who already runs registries and secret stores.
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Navigating the Hugging Face Hub: Models, Datasets, Spaces, and How to Read a Model Card (Hugging Face Series, Part 2)
The model card is provenance and license metadata you must vet before any model enters your environment. Here is how an infrastructure engineer reads the Hub fast and decides what to reject.
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What Hugging Face Actually Is: the Hub, the Libraries, and the Map (Hugging Face Series, Part 1)
Hugging Face is a registry, a set of open-source libraries, and a company. For infrastructure engineers moving into AI, here is the whole platform mapped onto systems you already run.
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NVIDIA NeMo Framework: Training and Fine-Tuning at Scale (NVIDIA AI Series, Part 22)
What the NVIDIA NeMo framework is: Megatron-Core parallelism, NeMo 2.0 Python recipes and NeMo-Run, Megatron Bridge for Hugging Face interop, and when to fine-tune instead of pretrain.
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NeMo Customization: LoRA, SFT, and RLHF on NVIDIA NeMo (NVIDIA AI Series, Part 23)
A practical decision guide for AI infrastructure architects on the full NeMo customization spectrum: when to use LoRA, full SFT, DPO, or GRPO, what data and GPU budget each method needs, and how the NeMo Customizer microservice ties it all together.
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Data Preparation at Scale with NeMo Curator (NVIDIA AI Series, Part 24)
NeMo Curator is NVIDIA’s GPU-accelerated data curation toolkit that runs exact dedup, fuzzy dedup, semantic dedup, heuristic filtering, classifier-based quality filters, and PII redaction at trillion-token scale using RAPIDS cuDF and Dask. Learn why investing in data curation beats buying more GPUs.
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VMware Private AI MLOps: Built-In Model Lifecycle vs DIY MLflow and KServe (Private AI Series, Part 22)
Two ways to run model lifecycle on VMware Private AI: the built-in Model Store and Model Runtime, or a DIY MLflow and KServe stack on VKS. Here is when each one wins, and the verdict.
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Deep Learning VMs in VMware Private AI Foundation: The Data Scientist Workbench (Private AI Series, Part 10)
What a Deep Learning VM in VMware Private AI Foundation actually is, how the image is built, the first-boot steps that quietly break deployments, and when to move off it to a VKS cluster.
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About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
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