Management in AI

Avoid These Mistakes When Considering ML Solutions

July 26, 2022
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Introduction

In the last two decades, data science and machine learning have moved from the fringes of academia to the corporate world. New developments, breakthroughs, and success stories are published almost daily. With the hype surrounding AI/ML, many companies and leaders want to jump on the bandwagon and implement ML. Some are worried that they are missing out on the latest trend and will lose to competitors who are ahead in the field. In this article, we’ll discover if your company should invest in machine learning projects and which common pitfalls to avoid.

Do I really need ML solutions?

Before investing, companies need to evaluate whether they actually need machine learning. It’s surprising how companies will blindly follow trends, investing large amounts into new projects, only to discover it doesn’t add any value for their customers. The best thing is to hold a product conversation and think about ideal outcomes. Ask yourself if machine learning can help achieve that outcome. Think about data you have available and what data would be required to train the model. You could also talk to an expert consultant to decide if a ML strategy is right for you.

Not understanding what the user needs and building technology for technology’s sake are two common failures when starting ML projects. To avoid this scenario, relentlessly focus on your customer needs and build products around these conversations (with or without ML embedded). Leadership can define objectives and key results for an organization, and teams can set the metrics to accomplish them. Perhaps your company might not benefit from using ML to segment customers, but you might achieve operational efficiency in your sales department instead. 

When you’re ready, make sure to set the right expectations from the start. Machine learning isn’t a magical solution that will solve your problems automatically. Rather it’s a tool able to create vast improvements in efficiency. Training a model takes time before it starts adding value for the company. It will require upfront investment and continuous development. However, with patience and commitment, machine learning can provide solutions that vastly outperform predecessors.

Common Mistakes When Applying ML Solutions

One common mistake is failing to find the balance between using existing solutions and building a custom ML architecture. On the one hand, building everything from scratch without experience might cost more time and money than anticipated. This can delay deployment of ML models into production or result in canceling the project before it’s completed. On the other hand, no existing solution on the market will perfectly solve a problem either, and it will always require fine-tuning.

As a general rule: large companies with bigger capital expenditures should seek to build a custom platform, whereas smaller companies should aim to leverage existing tools on the market. If your company is planning on embedding ML into multiple systems, you want to consider building a platform that makes it easy to start new ML projects. Smaller companies can start by implementing open-source solutions and fine-tuning them until it solves the problem.

Another big mistake is considering AI/ML as the entire product. A model is just one of many components needed to build a solution. Other components include: creating a data pipeline, organizing a data warehouse, installing documentation and review tools, building an interface. This means you need data scientists with different areas of expertise (analysts, engineers, operations). Machine learning systems require cross-team collaboration and will need input from other domain experts, not just the ML experts.

How can Vectice help

Vectice captures the most valuable assets of AI/ML projects and stores them in one central place. Datasets, code, notebooks, models and runs are memorialized, annotated, and become searchable and reusable. This allows companies to capture the value of their machine learning strategy and gradually build a foundation for future ML projects.

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