This article provides a detailed guide to implementing version control in Machine Learning Operations (MLOps), accessible through the Towards Data Science platform.
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A Step-by-Step Guide to Versioning in MLOps
Stay ahead of the competition by implementing Version Control in your AI strategy. This practice is crucial for managing your Data, ML Models, and Code effectively.
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- Identify Automation Opportunities: Pinpoint crucial customer touchpoints that AI can enhance.
- Define KPIs: Set clear metrics to measure the success of your AI initiatives.
- Select an AI Solution: Choose tools that suit your specific needs and offer flexibility.
- Implement Gradually: Begin with a small-scale pilot, analyze the results, and scale up your AI integration wisely.
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