Every machine learning solution needs a good algorithm to power it. Tech sites often write about advances in deep learning, and how the newest models are driving business success for everything from personalized shopping to national security. There are plenty overwrought analogies used to drive this point home. Data is the oil; the model is the car. Data is the ingredient; the algorithm is the recipe. The point is: neither works without the other.
In this guide, we’ll cover everything you need to know about creating the training data necessary to drive successful machine learning projects.
What gets far less press is what powers these algorithms: the data itself.