The Complete Decision Framework for ML Model Deployment: Edge, Cloud, Custom Models, and Model as a Service (MaaS)
A model can be accurate in a demo and still fail in the product. It may respond too slowly for […]
Deployment
EfficientNet Made Easy: Accuracy Through Principled Scaling
Paper: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (Tan et al., 2019) Imagine you are building a factory that
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How to Evaluate Agentic Systems: Methods, Metrics, and Best Practices
Imagine hiring a new teammate who can search the web, write code, call APIs, and make decisions without asking for
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ONNX Runtime Compaction: How to Make Browser ML Fast, Lightweight, and Practical
Imagine trying to deliver a single cup of coffee by sending an entire coffee shop to a customer’s home. That
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Deploying ONNX Models Made Easy: A Practical Step-by-Step Tutorial
ONNX (Open Neural Network Exchange) is a standard, open-source format for representing machine learning models as a computation graph. In
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ML System Design Case Study: YouTube Video Recommendation Engine
This article is a system-design blueprint for a YouTube-like recommender: how it serves results in milliseconds, how it learns from
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LLM Deployment: A Strategic Guide from Cloud to Edge
Imagine you have just built a high-performance race car engine (your Large Language Model). It is powerful, loud, and capable
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A Stakeholder’s Guide to the Machine Learning Project Lifecycle
This guide provides a comprehensive overview of the machine learning (ML) project lifecycle, designed to align stakeholder expectations with the
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From Prompts to Production: The MLOps Guide to Prompt Life-Cycle
Imagine you’re a master chef. You wouldn’t just throw ingredients into a pot; you’d meticulously craft a recipe, organize your
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The Real Cost of LLMs: Challenges from Research to Production
While Large Language Models (LLMs) offer powerful capabilities, deploying them in production introduces complex challenges across technical, operational, security, ethical,
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