New Video: AI-DevOps – How to End High AI Attrition Rates

    David Hughes



    Organizations expend a lot of time, money, and effort on AI and data science projects. And with good reason. Yet many are struggling to realize the technology’s full potential.

    All too often, good initiatives get stuck in “proof of concept Purgatory”. For many, taking promising AI models from the lab and applying them to real-world situations presents a significant hurdle. But why?

    Lack of coordination between data scientists and development teams. Ineffective monitoring and feedback. Weak organizational governance and control measures. Any one of these issues can prevent a high-quality AI system from making it to the production phase.

    To combat this, many organizations are turning to AI-DevOps.

    The Three Pillars of AI-DevOps

    AI-DevOps marks a revolutionary new approach to AI project deployment. And, during last month’s D4Global virtual event, I hosted a remote seminar outlining how it helps tackle attrition rates in AI project deployment.

    Also known as ML-Ops and Enterprise AI, AI-DevOps bridges the gap between data scientists and development teams, ensuring everyone is working towards the same goal. Supported by a process of continuous development and refinement, this enables you to deliver carefully controlled and trusted AI models faster.

    The methodology is built around three core pillars, each of which can be tailored to align with your specific requirements. These pillars are:

    1. Process and organization
    2. Skills and people
    3. Automation and infrastructure

    Real-world Applications

    Unless you can bring your AI models to bear on real world situations, they won’t provide value. To illustrate this point, I highlighted some of the AI-DevOps projects we’ve worked on in the past.

    These cover a diverse array of applications: from huge infrastructure projects for blue-chip companies, to small yet vital automation projects in the pharmaceutical industry. This helps us demonstrate the scalability of the AI-DevOps approach and how it can accelerate AI deployment – allowing you to take exciting proof of concepts and turn them into revolutionary initiatives that provide real-world value.

    Three Stages White Paper