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Ravi Singh - Unravelling Hidden Technical Debt in ML: A Pythonic Approach to Robust Systems
Uncover the hidden technical debt in your machine learning systems and learn a Pythonic approach to building robust and transparent models that avoid mistakes and biases.
- Technical debt is a concept that is not limited to software engineering, but also applies to machine learning.
- Technical debt is hard to identify and quantify, but it can be managed by having a good data platform, using automation, and setting up robust systems.
- In machine learning, technical debt can lead to mistakes, such as underestimating the complexity of tasks, and can be addressed by having a good understanding of the data and its dependencies.
- Communication is key in managing technical debt, and teams should work together to identify and address issues.
- Automation can help in managing technical debt, but it is not a magic solution and requires careful consideration.
- Technical debt is not limited to machine learning, but it is particularly challenging in this field due to the complexity and interdependence of tasks.
- Machine learning models should be transparent and explainable, and teams should work to improve their models and identify biases.
- Technical debt is a continuous process, and teams should work to identify and address issues regularly.
- Automation can help in managing technical debt, but it requires careful consideration and planning.
- The concept of technical debt is not new, but it is particularly relevant in machine learning due to the complexity and interdependence of tasks.
- Technical debt can lead to mistakes and can be avoided by having a good understanding of the data and its dependencies, and by working together as a team.
- Technical debt is not a question of “is it good enough?”, but rather a question of “is it accurate enough?”.
- Machine learning models should be transparent and explainable, and teams should work to improve their models and identify biases.
- Technical debt is a continuous process, and teams should work to identify and address issues regularly.
- The concept of technical debt is not limited to machine learning, but it is particularly relevant in this field due to the complexity and interdependence of tasks.
- Technical debt can lead to mistakes and can be avoided by having a good understanding of the data and its dependencies, and by working together as a team.