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@faun shared a link, 1 year, 2 months ago

Read Golang Weekly

Golang Weekly Newsletter, Gopa. Curated Golang news, tutorials, tools and more! 

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@emile shared a post, 1 year, 3 months ago
Co-founder, Nebuly

Tutorial on Dynamic GPU Partitioning with MIG to Maximize the Utilization of GPUs in Kubernetes

Partitioning is a way to divide GPU resources into smaller slices. This allows Pods to be scheduled only on the memory/compute resources they actually need, thus increasing GPU utilization and reducing infrastructure costs in Kubernetes clusters.

nos, opensource to maximize GPU utilization in Kubernetes
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@eon01 shared a post, 1 year, 5 months ago
Founder, FAUN

Announcing Kala: AI/ML Weekly Newsletter

We are excited to announce the imminent launch of Kala (AI/ML Weekly), the 13th newsletter in our series of developer-focused newsletters.

AI/ML Weekly Newsletter
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@gurubaran shared a post, 2 years, 7 months ago
Software Enthusiast, CloudFabrix

What is Observability and why you may need it

What is meant by Observability and how has it evolved into something which has transformed the entire IT ecosystem. We will break down all the information in this blog. Before Agile methodology was born, developers also used something called Test-Driven Development

What is Observability and why you may need it
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@mariah shared a post, 2 years, 8 months ago
Content & Community, FAUN

MLOps vs AIOps

There is a tendency to confuse MLOps and AIOps. While there are some common characteristics between the two, MLOps and AIOps are two different domains, are applied differently, and serve different goals.

MLOps_vs_AIOps.width-730.format-webp-lossless.png
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@eon01 shared a post, 2 years, 8 months ago
Founder, FAUN

Top 8 Managed MLOps Platforms

MLOps aims to make machine learning workflows more flexible, scalable, and manageable. The tools highlighted in this article are some of the best managed tools available in the MLOps market to help achieve this in your workflow.

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@windy shared a link, 10 months, 1 week ago

Top 10 proven approaches to Package ML Model & Deploy for inferencing

Deploying machine learning models into production requires selecting the right platform based on your needs. Managed cloud services provide end-to-end scaling while open source options offer flexibility. With proper preparation, your model can serve real-time, low-latency predictions at scale.

Top 10 proven approaches to Package ML Model & Deploy for inferencing