Machine Learning: Hidden Technical Debts and Solutions

Machine Learning: Hidden Technical Debts and Solutions

As machine learning systems find wide-spread adoption, solving complex real-words problems across various industries (Automotive, BFSI, Entertainment, Medical, Agriculture, …), improving and maintaining these systems overtime is becoming expensive and difficult, than developing and deployment.

machine learning systems find wide-spread adoption, solving complex real-words problems across various industries (Automotive, BFSI, Entertainment, Medical, Agriculture, …), improving and maintaining these systems overtime is becoming expensive and difficult, than developing and deployment. Long term maintenance of these ML systems is getting more involved than traditional systems due to the additional challenges of data and other specific ML issues [1]. In this article, we have covered a few hidden technical debt of the ML system which is summarized in the below table with some possible mitigation strategies.

Image for post

A

bstraction in software engineering is one of the best practices for the maintainable system. Strict abstraction boundaries help express the invariants and logical consistency of inputs and outputs for a given software component. But it’s difficult to enforce such strict abstraction boundaries in the ML system, as intended behavior of the system is learned using data and there is little way to separate abstract from quirks of data. [3]

Image for post

Entanglement

To make this concrete, let say we have an ML system that uses features f1, … , fn in a model. Since the change in feature distribution or adding new features or deleting existing features changes the importance weights of all features, as well as the target output. This is referred to as the Changing Anything Changes Everything (CACE) phenomenon, and applies not only to input features but also to hyper-parameters, learning settings, sampling methods, convergence thresholds, data selection, and essentially every other possible tweak [2].

To mitigate this, one possible solution is to detect prediction change as they occur and diagnose the cause of change, one reason for this prediction change could be a change in feature distribution (drift) which can be found using a tool like TensorFlow Data Validationwhich supports schema skew, feature skew, and distribution skew detection. For example, as shown in the below image by visualizing the distribution of feature values, we can catch these problems with feature distribution.

machine-learning tensorflow design software-engineering deep-learning

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Learn Programming, Software Engineering, Machine Learning, And More

Best Free Resources to Learn Programming, Software Engineering, Machine Learning, And More All you need to learn. Do you know that you can take the courses from MIT, Stanford.

Hire Machine Learning Developers in India

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.

ML Optimization pt.1 - Gradient Descent with Python

In this article, we explore gradient descent - the grandfather of all optimization techniques and it’s variations. We implement them from scratch with Python.

Applications of machine learning in different industry domains

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.

Hire Machine Learning Developer | Hire ML Experts in India

We supply you with world class machine learning experts / ML Developers with years of domain experience who can add more value to your business.