Your team has identified Machine Learning as a clear differentiator and you’re ready to deploy your resources and build the required capabilities. But what do you start with?
You can’t simply ask your software engineers to become data scientists. More so, building good ML models and running them in production requires a different approach to your normal applications, so you’ll need to adapt your DevOps practices (we call these new practices MLOps).
This program, which we ambitiously call “A World Class Machine Learning Practice”, aims to help you define your hiring and training goals, select the right platform that will integrate with the rest of your technology stack, and put structure to your new ML processes.
We will focus on three core components to create your world class ML practice:
- People. We will first need to understand the capabilities that you already have within your organisation. Most likely, you already have a number of rockstars in your team that will be keen to take on new challenges. We’ll help you define a training and ramp-up plan for them. We’ll also help you create new roles in your organisation, such as Data Scientist or Data Engineer, and outline the skillsets required and their seniority level.
- Platform. The purpose of a successful machine learning platform is not to re-engineer everything that you do and change the toolset with which you are familiar. We’re going to architect your platform extending your existing stack and including the ML toolset that your data scientists will love (core libraries such as TensorFlow, IDEs such as Jupyter Notebook or SageMarker Studio, etc).
- Processes. While you might be tempted to align your ML processes (such as model creation, monitoring or deployments) with your standard DevOps practices, this is not always the most productive approach. The lifecycle of a Machine Learning model is very different from a normal application. Development patterns are not as linear and correct monitoring is more subtle. We’ll introduce new processes that extend your DevOps strategy to account for these nuances of Machine Learning.