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Technical Debt and development velocity defined

Development Velocity

Machine learning development velocity refers to the speed and efficiency at which machine learning (ML) projects progress from the initial concept to deployment and maintenance. It encompasses the entire lifecycle of a machine learning project, from data collection and preprocessing to model training, evaluation, deployment, and ongoing optimization. In platform engineering this is often referred to as rate of change.

Technical Debt

The term "technical debt" in software engineering was coined by Ward Cunningham, Cunningham used the metaphor of financial debt to describe the trade-off between implementing a quick and dirty solution to meet immediate needs (similar to taking on financial debt for short-term gain) versus taking the time to do it properly with a more sustainable and maintainable solution (akin to avoiding financial debt but requiring more upfront investment). Just as financial debt accumulates interest over time, technical debt can accumulate and make future development more difficult and expensive.

The idea behind technical debt is to highlight the consequences of prioritizing short-term gains over long-term maintainability and the need to address and pay off this "debt" through proper refactoring and improvements. The term has since become widely adopted in the software development community to describe the accrued cost of deferred work on a software project.

Technical Debt in Machine Learning

Originally a software engineering concept, Technical debt is also relevant to Machine Learning Systems infact the landmark google paper suggest that ML systems have the propensity to easily gain this technical debt.

Machine learning offers a fantastically powerful toolkit for building useful complex prediction systems quickly. This paper argues it is dangerous to think of these quick wins as coming for free. Using the software engineering framework of technical debt , we ﬁnd it is common to incur massive ongoing maintenance costs in real-world ML systems Sculley et al (2021) Hidden Technical Debt in Machine Learning Systems

As the machine learning (ML) community continues to accumulate years of experience with livesystems, a wide-spread and uncomfortable trend has emerged: developing and deploying ML sys-tems is relatively fast and cheap, but maintaining them over time is difﬁcult and expensive

Sculley et al (2021) Hidden Technical Debt in Machine Learning Systems

Technical debt is important to consider especially when trying to move fast. Moving fast is easy, moving fast without acquiring technical debt is alot more complicated.

The Evolution Of ML Platforms

DevOps -- The paradigm shift that led the way

DevOps is a methodology in software development which advocates for teams owning the entire software development lifecycle. This paradigm shift from fragmented teams to end-to-end ownership enhances collaboration and accelerates delivery. Dev ops has become standard practice in modern software development. The adoption of DevOps has been widespread across various industries, with many organizations considering it an essential part of their software development and delivery processes. Some of the principles of DevOps are:

Automation
Continuous Testing
Continuous Monitoring
Collaboration and Communication
Version Control
Feedback Loops

Platforms -- Reducing Cognitive Load

This shift to DevOps and teams teams owning the entire development lifecycle introduces a new challenge—additional cognitive load. Cognitive load can be defined as

The total amount of mental effort a team uses to understand, operate and maintain their designated systems or tasks.

Skelton & Pais (2019) Team Topologies

As teams grapple with the mental effort required by adopting DevOps of understanding, operating, and maintaining systems, cognitive load becomes a barrier to efficiency. The weight of this additional load can hinder productivity, prompting organizations to seek solutions.

Platforms emerged as a strategic solution, delicately abstracting unnecessary details of the development lifecycle. This abstraction allows engineers to focus on critical tasks, mitigating cognitive load and fostering a more streamlined workflow.

The purpose of a platform team is to enable stream-aligned teams to deliver work with substantial autonomy. The stream-aligned team maintains full ownership of building, running, and fixing their application in production. The platform team provides internal services to reduce the cognitive load that would be required from stream-aligned teams to develop these underlying services.

Skelton & Pais (2019) Team Topologies

Infrastructure Platform teams enable organisations to scale delivery by solving common product and non-functional requirements with resilient solutions. This allows other teams to focus on building their own things and releasing value for their users

Rowse & Shepherd (2022) Building Infrastructure Platforms

ML Ops -- Reducing technical debt of machine learning

The ability of ML systems to rapidly accumulate technical debt has given rise to the concept of MLOps, a methodology that takes inspiration from and incorporates best practices of the DevOps , tailoring them to address the distinctive challenges and workflows inherent in machine learning in an effort to control technical debt. MLOps applies the established principles of DevOps to machine learning, recognizing that merely a fraction of real-world ML systems comprises the actual ML code. Serving as a crucial bridge between development and the ongoing intricacies of maintaining ML systems. MLOps is a methodology that provides a collection of concepts and workflows designed to promote efficiency, collaboration, and sustainability of the ML Lifecycle. Correctly applied MLOps can play a pivotal role in ensuring the efficiency, reliability, and scalability of machine learning implementations over time.

The Rise of Machine Learning Platform

The paradigm shifts of DevOps, MLOps and Platform Thinking led to the emergence of Machine Learning platforms. ML platforms are the application of MLOps concepts and workflows and provide a curated developer experience for Machine Learning developers throughout the entire ML lifecycle. As the ML team grows, the benefits of a platform amplify, creating a multiplier effect that allows organizations to scale whilst maintaining quality and not getting bogged down with technical debt.

Scribd's ML Platform -- MLOps in Action

At Scribd we have applied concepts from DevOps to our ML Operations in the following ways

Automation:
- Applying CI/CD strategies to model deployments through the use of Jenkins pipelines which deploy models from the Model Registry to AWS based endpoints.
- Automating Model training throug the use of Airflow DAGS and allowing these DAGS to trigger the deployment pipelines to deploy a model once re-training has occured.
Continuous Testing:
- Applying continuous testing as part of a model deployment pipeline, removing the need for manual testing.
- Increased tooling to support model validation testing.
Monitoring:
- Monitoring real time inference endpoints
- Monitoring training DAGS
Collaboration and Communication:
- Feature Store which provides feature discovery and re-use
- Model Database which provides model collaboration
Version Control:
- Applyied version control to experiments, machine learning models and features