Today, every technology startup needs to embrace AI and machine learning models to stay relevant in their business. Machine learning (ML), if implemented well, can have a direct impact on a company’s ability to succeed and raise the next round of funding. However, the path to implementing ML solutions comes with some specific hurdles for start-ups.
Let’s discuss the top considerations for getting ML models production-ready and the best approaches for a startup.
Availability of Data
An ML model is only as good as the data used to train it. For most startups, the biggest challenge is obtaining enough data related to the business problem they are trying to address in order to train the model sufficiently. Generic datasets are not useful when it comes to solving the unique and often complex problems that startups typically focus on.
One approach is to start with a simple ML model that can work with sparse data, refine the output with rule-based extraction techniques and roll out the model as a subset of the feature to customers. Then improve the model by setting up a pipeline for a collection of labeled data. Techniques such as data fingerprinting using autoencoders can also be used to incrementally develop the ML model.
Choice of Model
With the spiraling popularity of neural networks and their success in face recognition and other object recognition problems, many startups try to implement neural networks to solve business problems. But deep learning networks require even larger amounts of training data than traditional ML models, which can