Model Monitoring Enables Robust Machine Learning Applications
According to the 2020 Gartner Hype Cycle for Artificial Intelligence, machine learning (ML) is entering the Trough of Disillusionment phase. This is the phase where the real work begins—best practices, infrastructures, and tools are being developed to facilitate the technology’s integration into real-world production environments. Today, ML technologies have secured a central role in many companies.
ML technologies also are beginning to gain footholds across industries as they become more widely adopted in enterprises. For example, advances in speech and natural language models are fueling growth in voice applications. Demand for ML talent continues to rise, too—jobs topping LinkedIn’s 2020 Emerging Jobs Report include machine learning as a uniquely required skill. A quick scan of Fortune 1000 companies offers a snapshot of the average ML engagement across industries.
Model deployment isn’t a destination—models need near-constant monitoring and retraining. Manasi Vartak, CEO of Verta.ai, points out that model degradation begins upon deployment. “In our experience helping organizations deploy hundreds of ML models,” she explains, “models begin to degrade the moment they get deployed. This is particularly true for models built on time-varying data, but it also holds for models built on so-called static data, like natural images, because the deployed model is used on new and unseen data.”
