'How to Build Machine Learning Algorithms that Actually Work' machinelearning technology
And even when the business problem is a good fit for machine learning, without a shared definition of what success looks like, projects can languish in experimentation mode for months while stakeholders wait for an idealized level of machine-like perfection that can never be reached.
Without these clear, shared definitions articulated at the outset, many worthy machine learning projects will never reach production and valuable resources will be wasted. Solve a business problem using machine learning and not just embark on a machine learning project for checking off the ML box.With a clearly defined business problem and targeted success metrics, your potential pitfalls get more technical.
Representative training data is also key: If your training data doesn’t reflect the actual datasets your model will encounter, you may end up with a model that won’t perform once you’ve reached testing or production. For example, some projects require batched predictions on a regular basis, while others need to generate and deliver predictions on-demand when an application makes an API request to predict using the model.
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