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🚀 Model Deployment
ML Operations, Production ML, Model Serving, Inference
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- Deploy ML Models from
Jupyter
Notebooks
reddit.com
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ML
Systems
Textbook
mlsysbook.ai
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PyTorch
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An
Empirical
Evaluation of Modern
MLOps
Frameworks
arxiv.org
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6h
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Machine Learning in Production? What This Really
Means
towardsdatascience.com
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20h
⚙️
Data Engineering
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Building AI-native
backends
– RAG pipelines, function calling, prompt
versioning
, LLM observability
dev.to
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22h
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DEV
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Chess
OCR
: End-to-end ML pipeline running in the browser
sbondaryev.dev
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19h
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🔬 Research-Grade Context Engineering This material
represents
high-performance prompt research results designed based on Advanced
Logical
Architectures. 📜 Ma...
freederia.com
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10h
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NLP
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Getting a custom
PyTorch
LLM onto the Hugging Face Hub (Transformers:
AutoModel
, pipeline, and Trainer)
gilesthomas.com
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12h
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PyTorch
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A true '
hello
world' LLM
pipeline
alganet.github.io
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Distributed training with
mlx
: tensor
parallelism
stefpi.net
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16h
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LLM-AutoDP
:
Automatic
Data Processing via LLM Agents for Model Fine-tuning
arxiv.org
·
6h
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.NET AI
Essentials
- The Core Building
Blocks
Explained
devblogs.microsoft.com
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18h
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From Model Training to Model
Raising
cacm.acm.org
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19h
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Deploy Kimi
K2.5
on a Mac mini M4 Cluster and Call
Penligent.ai
: The Minimal Local-First Agentic Hacker Tutorial
penligent.ai
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2h
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lingbot-world-base-cam
灵波世界模型
modelscope.cn
·
4h
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A knowledge management system inspired by
plain-text
accounting
thalo.rejot.dev
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3h
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NLP
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Concept
Drift
in Production ML: When Your Model’s Rules Become
Obsolete
pub.towardsai.net
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6h
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Emmi-AI/noether
: Deep-learning framework for Engineering AI. Built on transformer building blocks, it delivers the full engineering stack, allowing teams to build, train, and operate industrial simulation models across engineering verticals.
github.com
·
18h
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Ultra-low-bit LLM Inference Allows AI-PC
CPUs
And
Discrete
Client GPUs To Approach High-end GPU-Level (Intel)
semiengineering.com
·
17h
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Building a
Scalable
AI-Powered Lead
Scoring
Engine: A Developer's Guide
dev.to
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2h
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Neural Networks
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