We’re embracing automation and all its facets. Machine learning sits atop the throne of buzzwords within the software development market. It feels like every day, there’s a new AI/ML product capable of immense impact. LLM, NLP, and a host of other acronyms are becoming commonplace in the vocabularies of tech professionals. The conversations and fervor regarding the promise and capability of these cutting-edge technologies are at an all-time high. However, there’s another side to this coin.

ML has this aura that it can solve any problem thrown at it. While there may be some truth to that, a bigger problem is that people don’t understand what ML cannot do. One mistake that people often make is thinking that ML projects are the same as regular software development projects. Although both are software, the way and outcome of them are quite different. Here are some of the main differences

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Differentiating between regular software development and ML is important for a variety of reasons, but here’s the main topic to understand. The business must understand exactly what they need. If you understand your problem statement intimately, you can better determine which avenue to travel down.

There are differences in workforce required, budgeting, timelines, and a host of other factors when choosing which option to pursue. Partnerships can be helpful in both scenarios, but it’s also integral that firms realize the foundational aspects necessary for success in either arena. It’s vital to have a comprehensive understanding of the project, its requirements, and other requisite expectations. Otherwise, you’ll turn right when you should’ve gone left, or vice versa.

Feasible Usage Of Machine Learning

Say your organization wants to start utilizing ML. Is there quality data that’s easily accessible? Do you already have a usable amount of data that could be used to train the model? If not, do you at least have a data collection process that can be refined? If the answer is no to any of these questions, then utilizing ML is still a ways away. There are certain foundational building blocks that must be present in order for a machine-learning project to be successful. Painting with broad strokes and assuming it’ll be an easy undertaking is the kind of mistake that’s accompanied by serious consequences.

This isn’t some doomsday prophecy, though. Creating these supplemental pieces that are then used in creating a bigger project is possible; it just requires the right amount of nuance and awareness. There’s no reason to shy away from ML due to some of these hurdles. In fact, the benefits only increase over time if you properly utilize automation meaningfully. But that only occurs when you understand the differences between traditional software development projects and those involving ML. It’s complicated, for sure, but that doesn’t mean it has to be overwhelming. There are plenty of uses for ML, and its impact is ever-growing. Just make sure you know how to walk before trying to run.