Get started with MLOps - inspiration session

Reflecting on 2023 through our IT lens, we can characterize it as the year of AI. The emergence of ChatGPT marked a significant breakthrough, granting widespread access to the power of AI. Consequently, an increasing number of organizations are keen to integrate AI into their workflows. While developing an AI solution is one aspect, managing it effectively is quite another. This is where MLOps plays a critical role.


The term ‘MLOps’, or Machine Learning Operations, goes well beyond the overly simplistic idea of ‘automating tasks in machine learning’, a common misconception. Various definitions exist, but we believe this one encapsulates the concept thoroughly:

“MLOps is a discipline that combines principles from DevOps and data engineering to manage the entire ML lifecycle, including data preparation, model training, deployment, monitoring, and maintenance.”


Admittedly, this definition may appear broad and complex, and it’s easy to feel overwhelmed by its scope. However, truly understanding MLOps involves recognizing the larger picture. How can MLOps enhance your workflow? How can it accelerate the time-to-market for AI solutions, among other benefits?

  • Level 1: manual processes
  • Level 2: automated development
  • Level 3: automated development and deployment

The suitable MLOps maturity level for your organization correlates with your overall data science maturity. Factors such as the number of implemented use cases and the size of your data science team play a critical role in this determination. These levels demonstrate that MLOps can be tailored to match each organization’s stage of growth. You can start simple and scale as needed.


Curious about where to start and how to progress? Our inspiration session is designed to guide you through these stages, helping you to identify the right MLOps maturity level for your organization and how to leverage it for maximum benefit. Join us to explore how MLOps can transform your workflows!