2020 showed us the limits of a passive approach to change. Whether data teams were automating simple reports or running complex systems of machine learning models, too much changed at once to avoid significant disruption. Those who did best had invested in a capability undervalued in the hype surrounding data and AI: resilience.
In this talk we use this need for resilience to answer the question: why invest in MLOps? Beyond the technical reasons that motivate data people (faster deployments, reduced manual work), data practitioners must answer this in business-friendly terms because long-term investments in resilience require short-term sacrifices in delivery.
Key Takeaways
- Wherever you are on your data journey, you will gain a broader understanding of the value of MLOps thinking, and hear success stories from the field that can inform your own progress, regardless of technology.
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