If you are new to the idea of building and deploying AI-based applications, you may not know the complexity of building, training, testing, deploying, and monitoring AI models or the benefits of having a no-code AI platform that can handle these tasks for you. Having a sophisticated machine-learning (ML) infrastructure and processes around it, can mean the difference between ML that is haphazardly deployed versus a ML deployment that is controlled and continuously monitored.
Why is this important? ML model development is still an inexact science, and things can go wrong. If something goes wrong, such as unexpected output, does it mean that the model is sound, but the training was inadequate? Or is there a fundamental flaw in the model design? Not having a well-defined structure and process in place makes it more difficult to track down why things go wrong and how to fix them.
In our most recent blog, ‘Why No-Code AI is Critical for Unblocking Enterprise AI’ we explain how Sway AI intends on becoming a major proponent in the acceleration of AI adoption with their No-Code Visual Platform, which automates a majority of the required AI tasks including those required for ML Ops, allowing enterprises to implement AI with ease. In this article, however, we dive a bit deeper into the importance of ML infrastructure and ML operations, and how a no-code AI platform can help streamline the definition of these important components of your AI development environment.
The term ML infrastructure refers to the set of tools and resources (both human and computing) that define your AI development environment. There is no single ML infrastructure setup that is appropriate for all AI development teams — each setup will be different, depending on the needs and capabilities of the organization and their long-term plans around AI.
That said, most ML infrastructure setups consist of some common components:
- A data pipeline for providing training and testing data for models in development
- A model training pipeline for preparing a model for its intended use
- A model scoring pipeline to evaluate a trained model against testing data that it has not “seen” previously
- A post-deployment performance evaluation pipeline to keep track of how well a model is performing in the production environment
An important aspect of ML infrastructure design is that your principal users — the data scientists who develop the models — expect the ML infrastructure to work for them and to be efficient. They want to manage the model’s lifecycle without getting bogged down in activities such as spinning up cloud servers or establishing communications protocols. Let the data scientists do what they do best without having them worrying about the fussy technical deployment details.
ML operations (ML Ops, for short) is analogous to the concept of development operations (DevOps) in conventional application development. It’s the set of policies, processes, and procedures that govern the management of a model’s lifecycle. ML Ops is especially important in large teams because a solid ML Ops environment makes it more likely that everyone will be on the same page and models will not be released to production before being thoroughly tested and vetted.
Important aspects of ML Ops include:
- What modeling frameworks, languages, and tools the team is using; a larger variety makes ML Ops more complicated
- Testing protocols that define what constitutes adequate testing and the performance standards that must be met before a model can be released in production
- Change management procedures to release new or updated models in a controlled fashion — and back out changes if needed
- Post-deployment monitoring to understand unexpected model output “in the wild”
ML Ops and ML infrastructure work hand-in-hand. Your ML infrastructure must be designed to support your ML Ops but should be sufficiently flexible to support changes in the ML Ops processes.
How the Sway AI No-Code Platform Benefits ML Infrastructure and ML Ops
Enterprises such as Uber, Netflix, and Booking.com have mature ML infrastructure and ML Ops built and refined over several years of use. Many of these enterprises built major components of their ML infrastructure from the ground up, although there are some commercial and open-source tools available as well.
Smaller enterprises, or those new to AI development, may not have the time or resources to build and refine ML infrastructures optimized for their situations. The good news for these organizations is that much of the effort can be streamlined by using Sway AI’s No-Code Visual Platform.
Sway AI’s platform provides capabilities like one-click model deployment, a view into model performance and input features, and model version tracking. By putting much of the technical detail in the background and exposing a slick drag-and-drop user interface for AI development, the Sway AI platform makes it possible to build ML infrastructure and automate ML Ops without requiring extensive knowledge of the underlying tools and technologies or how to interconnect them. As a result, enterprises can get started with AI development much faster, without relying on high-priced ML infrastructure engineers and consultants.
Furthermore, Sway AI has model quality monitoring capabilities, so deployment and maintenance of your end-to-end AI applications are ensured. Issues such as training-serving skew, concept drift, and upstream data issues that can cause degradations are monitored to reduce and mitigate incidents of silent failures.
You still need some idea of what you want to achieve with AI and a long-term roadmap for your AI-based applications, but even this exercise is much easier when you do not need to worry about the technical details of the deployment.
For more information about how the Sway AI platform can support your ML infrastructure and ML Ops, contact Sway AI today at firstname.lastname@example.org.