Fundamentally world-changing AI technology
As artificial intelligence (AI) gains importance, it’s creating amazing results across many industries. From retail sales forecasting to supply chain issue resolution to potential disease prediction to customer service automation, there are endless opportunities.
Every department in every company wants some aspect of AI to drive business value. The technology is fundamentally world-changing. Its invention can be equated to that of the lightbulb.
Moving AI/ML models from development to production
Enabling the machine learning (ML) models that drive AI is no easy feat. And deploying AI solutions in production is challenging. If business stakeholders and technologists struggle to collaborate effectively, resulting investments in AI can fail to address the business need. Too often, the focus of data science teams can lie more on designing and deploying highly accurate AI/ML models than working with business and product teams to ensure end-to-end orchestration with business workflow solutions. Machine learning operations (MLOps) can become costly. By redefining the framing of MLOps, organizations can better meet the needs of the business and drive value.
Value arrives in a calculated, ongoing process
MLOps can tie models to business value. However, AI is not a typical technology deployment. ML models need to be observed with feedback loops to ensure optimal capabilities. It’s not a “once and done” scenario. It’s a calculated, ongoing process—and a mindset—that gives data science teams a structured way to rapidly develop, deploy, monitor, and maintain AI/ML solutions that make a real impact on the business. It is not a single tool or technology.
MLOps is an end-to-end AI/ML life cycle management approach necessary for governance and agility. And, it needs to have guardrails in place.
MLOps carries real impact when executed right
Artificial intelligence carries with it not only the standard deployment tactics but also a whole new set of challenges. It requires a monitored, end-to-end management life cycle with the guardrails and business value checks to keep it on track. It also requires focused change management in good measure.
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