What do practitioners see as the main challenges in Operationalising Machine Learning solutions?

Earlier this year, some of our experts from the data service gave a talk for Data Idols on our experiences working with data scientists to move machine learning models into operations.

The audience was largely data scientists from lots of organisations (mostly not Equal Experts), and so we took the opportunity to find out what their concerns were with a simple survey question: What do you see as the main challenges with machine learning? There were around 100 attendees and people could vote for as many of the challenges as they wanted.

You can see the results in the chart here:

Machine Learning experts do not generally see the development of the model as the difficult bit. I suspect that in fact this is the part they love best about the job.  Also, most data specialists are familiar with the right techniques for different modeling problems and I think love the data exploration and feature engineering tasks.

Where they have the most challenges are in integrating a validated model into business operations and managing it once it’s there. We know this is a non-trivial job – and data scientists usually need support from other technical functions.  Our MLOps playbook was developed to help practitioners understand and address the different aspects of operationalising Machine Learning models.

The other area where they had most challenges is in accessing data. Since machine learning is fundamentally dependent on data,  this is no surprise. Our  data pipeline playbook was created to help data professionals create reliable access to data sources – it is certainly essential to put some of the practices in place for production models.