alirezadir/production-level-deep-learning

★ 4,632Audience · developerComplexity · 3/5Setup · easy

Mindmap

mindmap
  root((Production ML))
    Pipeline stages
      Data labeling
      Model training
      Evaluation
      Deployment
      Monitoring
    Key tools
      Experiment tracking
      Serving frameworks
      Data storage
    Common pitfalls
      Unclear goals
      Poor scoping
    Audience
      ML engineers
      AI teams

mindmap root((Production ML)) Pipeline stages Data labeling Model training Evaluation Deployment Monitoring Key tools Experiment tracking Serving frameworks Data storage Common pitfalls Unclear goals Poor scoping Audience ML engineers AI teams

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Things people build with this

USE CASE 1

Plan a full production ML pipeline from data collection through model deployment and monitoring.

USE CASE 2

Evaluate which MLOps tools fit your team's workflow for experiment tracking, serving, and data storage.

USE CASE 3

Identify the most common reasons AI projects fail before reaching production and address those gaps early.

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a structured guide for teams and engineers who want to take an AI model beyond a research experiment and get it actually running in a real product. Training a model that scores well on a test dataset is only the beginning. Getting it to work reliably in production, at scale, with real users, requires a whole additional layer of engineering. This guide documents what that layer looks like. The content walks through the full pipeline a production AI system needs: how to gather and label training data, how to store and version it, how to organize the training process, how to evaluate whether a model is good enough to ship, how to deploy it so it can respond to requests, and how to monitor it over time so you notice when it starts failing. Each section recommends specific tools and platforms that practitioners commonly use at each stage, with notes on trade-offs. Some of the topics covered include data labeling platforms for building training datasets, storage options for large collections of images or text, workflow tools for automating the steps between raw data and a trained model, experiment tracking so you can compare different training runs, and serving frameworks for making a model available over an API. The guide also notes that 85 percent of AI projects never reach production, and outlines common reasons, including poorly scoped goals and unclear success criteria. The material is drawn from courses and workshops given at Berkeley, OpenAI, and industry meetups, and the repository links out to those sources. There is no code to run. The repository is a reference document, meant to be read and consulted during the planning and engineering phases of a machine learning project. A companion repository on machine learning interviews is also mentioned for those preparing for technical hiring processes.

Copy-paste prompts

Prompt 1

Walk me through the steps to take a trained Python ML model from experiment to a reliable production API, including serving and monitoring, following the production-level-deep-learning guide.

Prompt 2

Recommend experiment tracking and model serving tools for a small team deploying their first ML model to a REST API.

Prompt 3

Create a checklist for getting a deep learning model production-ready, covering data versioning, evaluation criteria, deployment, and alerting.

Prompt 4

What are the most common reasons AI projects never reach production and what should I do to avoid each one?

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