Model-based machine learning and AI, although new, are rapidly becoming a mainstream technology in all large enterprises. The use of such models has drastically increased worldwide. Siri in iOS, Stock price prediction, chatbots, facial recognition, and detection are a few examples involving machine learning and Artificial Intelligence.
Despite all the high-level models prepared by combining AI and Machine Learning, implementing all the technologies at scale becomes challenging. This is where MLOps comes into the picture.
Features of MLOps
- MLOps is a set of practices that combine Machine Learning, DevOps, and Data Engineering to deploy and maintain ML systems in production reliability and efficiency.
- It brings the team of data scientists who curate and datasets and builds AI models that analyze them into the picture.
- It enables data science, and IT teams to collaborate and increase model development and deployment pace via monitoring, validation, and machine learning models.
- It allows you to track / version/audit / certify / re-use every asset in your ML lifecycle and provide orchestration services to streamline managing this life cycle.
Practical Implementation of MLOps
MLOps is a recent trend in the IT industry, and yet, it has found its usage, and developers are inclined towards implementing it. Recent practical implementation examples involve a PC maker that developed software using AI to predict whether its laptops would need maintenance soon to install software updates required automatically. Using established MLOps practices and internal specialists, the OEM wrote and tested its AI models on a fleet of 3,000 notebooks.
MLOps will be bringing a huge curve to the IT industry, creating more useful and extensive projects taking AI to the next level.