It’s been a while since I shoehorned a sport I enjoy watching into a work related blog post, but fear not, I am back.
You might remember my blog on Data Engineers, Recruitment and Formula 1 sometime ago?
Well my enthusiasm for F1 has not waned, in fact it’s only become more obsessive.
However, since that blog I have moved out of data recruitment and into the world of Machine Learning Operations (MLOps), working for fuzzy labs, open source MLOps extraordinaires.
At fuzzy labs we often use the analogy that a Machine Learning Model is like a V6 F1 engine (well, usually we just say high performance engine, but it doesn’t fit as nicely), but if you have no MLOPs infrastructure in place, you’re essentially leaving that engine on the garage floor - which is pretty stupid right?
In my opinion, F1 has a lot of parallels (or at least enough to write another blog about!) with Machine Learning in terms of different disciplines that are needed to win a race, or indeed productionise an effective machine learning solution.
The engineers designing and building the engine back in the factory are analogous to the Data Scientist creating machine learning models. They don’t know what the optimum car set-up or design will be when they start off, so they need the right tools in place to be able to experiment, test, collaborate with each other effectively. In F1 they are constantly tweaking things, designing new parts, tweaking the set-up of the car to maximise performance - much like a machine learning model. And like in machine learning, they don’t always get it right - just look at the Mercedes car in 2022 for an example of something working in theory, but not in production/on the track!
Those engineers need the help of a wider team in order to set the driver and indeed race team up for success. In F1 the race day team is usually made up of aerodynamicists, the pit crew, the strategists and mechanics. Just as in machine learning, to set the data science team and as a result, the wider business up for success, you need to think about your: strategists (or monitoring and experiment tracking); aerodynamicists (or training and deployment pipelines); the pit crew (managing your data).
In F1 the team with the most powerful engine doesn’t always win the race, or indeed the championship. There is no point in having all that power if you don’t know how to harness it or have the rest of the component parts in place to unleash the full potential of the car - just look at Charles Leclerc in Monaco or Baku recently who had the best engine but not the correct strategy or reliability.
This is equally true in the world of machine learning. The biggest and most clever model doesn’t necessarily equal business value. Having a solid MLOps infrastructure ensures you are making the most of your engine (or model …) and by actually deploying, monitoring and tracking these models, you not only get a fast start, but you have long term reliability for the long racing season ahead!
Now this is all great, but where do you even start?! Luckily for F1 teams they have huge amounts of experience, resources and data to try and get this stuff right. That is a luxury not all companies looking to unleash their AI have.
That is where we come in! As an extension of your data team, fuzzy labs become your inhouse open source MLOps experts. We work with customers of all shapes and sizes to really understand their MLOps pain points, and through our extensive research of the current tooling landscape, we can provide a detailed report of our recommendations. Following that initial Discovery phase, we work closely with your team to build a proof of concept or MVP and we’re on hand to support you for as long as you need us.
Check out our blog for more information on the tooling and our approach - and for a no obligation chat, get in touch: email@example.com