When it comes to developing, assessing and remodelling Machine Learning solutions, there’s one overarching goal in mind: Delivering business value by driving growth and efficiency.
But where do you even start? And how do you know if the time you spend on a project is going to be worthwhile?
Here are five key considerations every Data Scientist, Analyst, and business should bear in mind when it comes to developing and deploying Machine Learning solutions.
Keep the business outcome in mind
When building Machine Learning solutions for a business, always look beyond the accuracy of models to determine whether they deliver real business value. The ultimate aim of a solution is usually to increase profit margins. Quantifying the amount that can be saved based on a model's performance is essential when convincing stakeholders to invest in a new project. It's important to make sure time’s not being spent on projects that have little to no business value.
Always consider the end productionised model
If you’re building and developing models to use in the real world, it's essential to consider the pipeline from the initial data and model to predictions and the end user. Questions like, ‘where’s the data coming from? Will it be accessible when you need it? Is there a risk of slowing down operational systems when productionising a use case? This means making sure the right teams of data engineers, database admins, etc., are involved in planning and building an accurate development timeline; minimising the risk of a promising model getting dumped on the shelf due to constraints on the external systems it relies on. It's also crucial to keep the business in mind, especially when considering the costs associated with running and training models. Often, model training can be computationally expensive, leading to high bills from cloud providers each month. Achieving the right balance of retraining for accuracy and getting results that benefit the business is critical to maximising the ROI from a model.
Think about the end user
The output of the model needs to be in a format that the end user can work with. Teams resist change, especially when they're used to ways of working that have been around for a long time. Bringing these teams in for discussions on how the end result is formed ensures that the models you've worked so hard to produce actually get used. While end users may resist change to their ways of working, they're the people who know the process better than anyone else, and they’ll likely have some valuable input that can help form your solution. This is a great way to give accountability to the end user to make use of the model, as they feel they've played their role in creating it.
Talk about data issues early on
Data sets are often not perfectly formed or readily available. Data can vary in quality dramatically, both in terms of accuracy, completeness, and accessibility. Communicating these issues early on in the process gives you the best chance to address and overcome them going forward. It also helps make sure stakeholder expectations are set with a timely explanation, keeping trust with the data-science team further down the line. It also allows companies to reform their data strategy so that, while a project may not be possible at present, steps can be taken to give a better chance of success in the future.
Assess your model beyond accuracy
Performance metrics that measure accuracy and bias are essential when assessing a model’s performance, but these metrics without context don’t give the full picture of the solution. You should also assess your model's performance further, linking it to the overall business outcome you're trying to achieve. For example, a demand forecast for non-perishable goods is predicting with 90% accuracy, but when assessing the bias of a model, you see that it tends to underpredict demand rather than overpredict. If you were to blindly follow this prediction when making stock orders, the business could end up with significant availability issues, particularly in busy seasonal periods.
To sum up...
No Machine Learning solution will be exactly the same as the last one. So there’s no one-way-fits-all approach to getting it right. But, by following the five key principles we discussed in this blog post, you can maximise the potential business value your model can deliver, helping you, your colleagues, and the leadership team to make more data-driven decisions that drive growth and success.