The AI Deployment Gap
≈ 3 min read
Why is it that—fueled by endless coffee—you can craft a robust machine learning model in mere weeks, yet it takes a team on average six to twelve months to deploy it? And why is it that, even then, only about 22%[1] of models make it into production?
This phenomenon is often referred to as the 'AI deployment gap', and it’s something I've personally witnessed many times while working with teams across various industries. Reasons vary, of course, but I collected a list of typical challenges that you should be aware of when transitioning your model to production.
Pre-Deployment Challenges
- Evaluating use-case and model deployment feasibility requires an in-depth analysis to ensure that the chosen model aligns well with the business objectives and can be integrated smoothly into existing systems and processes
- Collecting necessary MLOps project requirements can be hard because we currently don’t have an established industry standard and Data Science teams often lack practical deployment experience
- Cutting through the market noise to discover the most appropriate MLOps tools and cloud services for the use case at hand, while also not overengineering the solution
Deployment Challenges
- Versioning and storing model artifacts, hyperparameters, and metrics together with productive code
- Building everything in a scalable, reliable, and cost-optimized way
- Implementing and maintaining state-of-the-art data security and encryption standards
- Setting up alarms and dashboards for monitoring the infrastructure and models
- Providing all the underlying Infrastructure as Code (IaC) to ensure reproducibility and enable versioning
- Writing integration and End-to-End (E2E) tests
- Working efficiently in a team with different backgrounds, following DevOps best practices like having a single source of truth with integrated CI/CD pipelines for automated deployments
- Building everything in a way that staging environments or new cloud infrastructure can readily be added later
I've observed teams cycling through countless tools and infrastructure combinations, often spending years to find a reliable setup. For those who succeed in this quest, the solutions tend to be notoriously complex and incur significant maintenance and operational costs.