ML Ops is Essential for Long Term AI Deployment Success

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ML Ops (machine learning operations) is an essential but often underrepresented step in the enterprise machine learning workflow. Data democratisation is one of the growing trends in AI, but this mainly focuses on creating models, not making them work in practice. What doesn't get talked about enough is the role explainability and ML Ops need to play to make AI deployments work in practice. If your product doesn't explain itself nor integrate ML Ops with Data Ops and IT Ops, then your AI deployment will struggle and ultimately do more harm than good.

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All Models Require Maintenance to Stay Relevant

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There are many factors which affect model accuracy, there could be bias in the data or business circumstances may have changed. Your business doesn't stay the same, so neither should your model. Without careful management and maintenance a once accurate model will deliver more risk than reward. You can't just deploy your models and then forget about them. 

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Choose a Product that Supports the Entire Enterprise Machine Learning Workflow

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The most crucial question is: how do you de-risk and get lasting business value when deploying AI? A product that democratises model creation must also democratise the enterprise deployment process. And have the facility to identify and carry out the iterative improvements needed to get long-term value from machine learning models. Data democratisation has many benefits, but in reducing the reliance on data scientists, don't ignore the vital role they play in deploying, tuning and maintaining models. Use a product that supports the entire enterprise machine learning workflow, not just part of it.

For these reasons we have made ML Ops a focus with Oscar Enterprise AI, with numerous features designed to aid the creation of machine learning models and their implementation. Among others, these include methods for:

•    Understanding the consequences of the bias in your datasets and model;
•    Understanding the impact of the model on computing resources;
•    Understanding how the app, model and data interact and how that interaction wanes over time.

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Check out our white paper for more information on the essential role ML Ops plays in enterprise machine learning deployment and how we've addressed this in Oscar.