MLOps - 3 months - Implementation

AE nv/sa

Build a MLOps platform to operationalize your models

In today's data-driven world, many businesses have already experimented with machine learning. However, these models often remain stuck in prototype purgatory. In the worst case, they gather dust on data scientists' laptops. In a slightly less dire situation, the models are deployed as prototypes but fail to gain the trust of business stakeholders. These models hold immense potential, but without proper deployment and continuous monitoring, they fail to realize any business value—or, worse, they degrade over time, leading to diminished business value. This is where a robust MLOps platform becomes essential. However, we don't just throw your models into Azure ML Studio and call it a day. We apply best practices from software engineering to ensure their reliability and maintainability, such as version control for all ML assets, dev / test / production environments, logging, and monitoring. We extend these with best practices specific for machine learning, such as model performance monitoring, A/B testing, and data quality checks. We have a proven approach consisting of 3 phases with a typical total duration of a few months, to onboard several models:

  1. Think: • Conduct comprehensive interviews with stakeholders to understand business needs, and how the ML models should be integrated into your existing data ecosystem. • Define a roadmap for an Azure-based MLOps platform implementation.

  2. Build: • We don’t start from scratch, but we have already developed assets that we can quickly deploy onto your Azure infrastructure. We can easily plug in your models into these assets. • Integrate your machine learning models into your operational or analytical flows. • Provide comprehensive documentation and training to your teams, ensuring they can effectively use and maintain the platform.

  3. Run: • Monitor and maintain your MLOps platform, if desired.

Our MLOps Platform service is a comprehensive solution that goes beyond setup to ensure your organization is equipped for the future, ensuring your models deliver continuous value. By choosing Azure ML Studio, you're not just adopting new technology; you're embracing a framework for continuous data-driven innovation and growth.