A Guide to Production Level Deep Learning
Overview
The project "Production-Level Deep Learning" is a comprehensive resource aimed at guiding engineers and developers in deploying deep learning systems in real-world applications. Deploying such models isn’t just about achieving good performance; it involves several distinct components that work together to form a production-ready system.
The guide acknowledges the challenges involved in transitioning deep learning models from the training stage to deployment and offers structured advice for overcoming these hurdles. The repo is constantly being updated and improved, welcoming feedback and contributions from its user community.
Introduction to Machine Learning Projects
Interestingly, around 85% of AI projects fail, often due to being technically infeasible, poorly scoped, or not making it to production. A successful AI project should clearly define success criteria, ensure good team management, and align with achievable technical goals.
ML Projects Lifecycle
Understanding the state of the art in your domain is crucial. This ensures that you know what's possible and what to try next in your machine learning (ML) endeavors. The project lifecycle encompasses several stages, from data management to model deployment and maintenance.
Mental Model for ML Project
The guide emphasizes two factors critical to defining and prioritizing ML projects:
- High Impact: Projects that focus on complex parts of the pipeline or automate manual processes.
- Low Cost: Projects that are feasible due to data availability, performance requirements, and manageable problem difficulty.
Full Stack Pipeline
The "Full Stack Pipeline" is an essential part of deploying deep learning models, comprising various components:
Data Management
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Data Sources: Supervised learning needs ample labeled data, which can be costly to gather. Resources include open-source data, data augmentation, and synthetic data.
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Data Labeling: This requires specialized platforms and potential temporary labor. Methods include crowdsourcing, hiring annotators, or using data labeling service companies.
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Data Storage: Options include object stores for binary data, databases for metadata, data lakes for aggregating features, and feature stores to manage and share ML features effectively.
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Data Versioning: This is crucial, as ML models are part code, part data. Without data versioning, model versioning isn’t possible. Tools like DVC, Pachyderm, and Dolt assist in managing data versions.
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Data Processing: Different sources contribute to data used in training production models. Processing is often orchestrated using workflow managers like Luigi and Airflow.
Development, Training, and Evaluation
- Software Engineering: Python remains the dominant programming language for deep learning. Various editors such as Vim, Emacs, VS Code, and developmental environments like Jupyter Notebooks are used to write and manage code.
Conclusion
Production-Level Deep Learning is a robust guide offering strategies, tools, and best practices for deploying deep learning systems effectively. It highlights the importance of understanding the entire lifecycle of an ML project, from data handling and versioning to choosing the right tools for deployment. With a blend of theoretical knowledge and practical insights, this resource is invaluable for anyone looking to navigate the complexities of bringing deep learning models to production.