An 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.
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Today, every technology startup needs to embrace and models to stay relevant in their business. Machine learning (), 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 solutions comes with some specific hurdles for start-ups.
Let’s discuss the top considerations for getting models production-ready and the best approaches for a startup.
Availability of Data
An 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 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 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 networks require even larger amounts of training data than traditional models, which can stall a project indefinitely.
A good starting point is selecting models based on regression, decision trees, and Support Vector Machine (SVM). After acquiring a large amount of training data, other models can be considered.
explainability is a key requirement for startups especially in the Security, FinTech and healthcare domains due to legal and regulatory requirements. If the is making decisions, the way in which the decisions are being made needs to be transparent and understandable by humans. If not, the model cannot be ‘trusted’ to perform accurately and without bias. Thus, the explainability of the model becomes relevant when the algorithm is making decisions where there is an element of risk or when it is trying to identify the root cause of a problem.
Consider a loan processing system that uses to approve or reject a loan application. A simple decision tree model might start with looking at how complete the application is, and then the credit rating of the applicant followed by the number of applicants and so on to reach a decision. A similar problem when solved using a deep neural network, the layers do not necessarily map to human recognizable features and hence hard to explain. […]