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Moving an AI project from ideation to realization is a vicious loop, and there is only one way to resolve it – don’t let the loop begin! That is true because data deserves expert handling at all levels. Starting with extracting it from different sources to cleaning, analyzing, and populating it, machine learning systems are prone to latencies if the underlying architecture lacks an operational approach to ML – known as MLOps.
Most AI projects do not make it to production due to a gap that sounds very basic but has a massive impact: improper communication between the data scientists and the business. This survey from IDC focuses on the importance of continuous engagements between the two verticals. It has compelled organizations to look for immediately available solutions, and that is where MLOps enters the scene.
MLOps best practices focus on:
* Providing end-to-end visibility of data extraction, model creation, deployment, and monitoring for faster processing.
* Faster auditing & replicating of production models by storing all related artifacts such as versioning data and metadata.
* Effortless retraining of a model as per varying environment and requirements
* Faster, securer, and accurate testing of the ML systems.
However, developing, implementing, or training ML models was never the main bottleneck. Building an integrated AI system for continuous operations in the production environment, without any major disconnects, is the actual challenge. For example, organizations that have to deploy ML solutions on demand have no choice but to iteratively rewrite the experimental code. The approach is ambiguous and may or may not end in success.
That is exactly what MLOps tries to resolve.
Put simply, DataOps for ML models is MLOps. It is the process of operationalizing ML models through collaboration with data scientists...