Introduction
When implementing data science or machine learning solutions, leaders must define and communicate the value it creates for a company. Reporting can happen on different levels, but the same principles will apply. In this article, we’ll cover tips and tricks for setting the right expectations to stakeholders and employees about the value of machine learning projects
What value does an ML solution provide?
Understand that the value of ML solutions is not always different from other solutions. Sometimes it helps to boost sales and increase revenue. Or it can achieve operational efficiency and lower production cost. It could deliver valuable insights to another branch of the company about competitors or customers. ML solutions can also predict economic trends from data which allows better planning for the future. These results can be achieved with traditional solutions, but machine learning will generally outperform them as they improve over time.
Building a foundational infrastructure can also add tremendous value. If your organization is planning on embedding ML into multiple systems, it pays to build a platform that makes it easy to start new ML projects. This includes building data storage and pipeline, training employees, automating data cleaning, enabling project reviews, etc. While these projects don’t directly add value, they can generate massive positive returns in the long run.
How is ML different from other solutions?
The main difference with ML systems is the presence of a feedback loop. This provides the ability to learn from data sets over time. In contrast with deterministic solutions (which have a target, start date and finish line), ML solutions can continue improving over time. This means that added value can keep increasing after the solution has been launched. But it will require monitoring the model after deployment, so consider investing in a knowledge capture solution.
That being said, machine learning projects are often research-based, and there is no guarantee for a successful outcome at the start of a project. Only a few solutions are common enough that they can be implemented in a standardized way. All other projects will go through trial-and-error to see what works and doesn’t. When communicating the value of ML projects, set the right expectations with stakeholders that investments might not generate any returns if a model fails.
To minimize the risk of missing out on value, consider the programmability of ML systems: open-source platforms and libraries built for the developer community, that make it faster and cheaper to deploy ML. This is particularly valuable for companies who are just starting out on their ML journey and don’t want to build the entire infrastructure from scratch.
How can domain experts add value to ML?
Much of the value from machine learning solutions comes from cross-team collaboration. While some experts fear that their jobs are being replaced with machines, this is still a very long way from reality. As it stands, machine learning does not offer replacement, but rather improvement. No model is good without expert input and training, and most models will be “dumb” at first.
ML models are a combination of technical skills and domain knowledge. ML teams are looking for opportunities to improve and augment systems, in collaboration with the domain experts. A language model will require linguistic experts, tailoring ads will require marketing and sales people, predicting economic trends will require financial experts, and so forth. The more domain experts are integrated into ML systems, the more value can be derived from its solutions.
How can Vectice help?
Vectice captures the most valuable assets of AI/ML projects and stores them in one central place. Many ML assets (datasets, code, notebooks, models, runs) are often created across multiple teams. If those results are not cataloged or people leave the company, that knowledge may be lost forever.
Vectice safeguards these valuable assets and provides access to the right people at the greatest moment of impact. Outcomes are memorialized, annotated, searchable and reusable. This allows companies to capture the value of their machine learning strategy and gradually build a foundation for future ML projects.
