You have finished solving a machine learning problem. The accuracy of your model is awesome. Till now your work is probably a Jupyter notebook, which is full of code, a few visuals, and very little documentation. If you see your work after a month or so, you might struggle to understand your own creation. To make matter worse, the Jupyter notebook does not have all decisions and assumptions you have taken in the solution.

You have finished solving a machine learning problem. The accuracy of your model is awesome. Till now your work is probably a Jupyter notebook, which is full of code, a few visuals, and very little documentation. If you see your work after a month or so, you might struggle to understand your own creation. To make matter worse, the Jupyter notebook does not have all decisions and assumptions you have taken in the solution.

Big data is around us. However, it is common to hear from a lot of data scientists and researchers doing analytics that they need more data. How is that possible, and where does this eagerness to get more data come from? Very often, data scientists need lots of data to train sophisticated machine-learning models. The same applies when using machine-learning algorithms for cybersecurity.

Mathematicians have discovered a problem they cannot solve. It's not that they're not smart enough; there simply is no answer. The problem has to do with machine learning -- the type of artificial-intelligence models some computers use to "learn" how to do a specific task. When Facebook or Google recognizes a photo of you and suggests that you tag yourself, it's using machine learning. Neuroscientists use machine learning to "read" someone's thoughts.

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. Think of your immune response as a giant machine-learning problem, with your body as the computer. Immune cells travel around your body, sampling all sorts of matter they come into contact with, from your own cells to the cells of organisms that definitely shouldn't be there. If immune cells encounter something they know shouldn't part of your body -- bacteria or a virus, say -- the body scales up the cells that know how to deal with that interloper.

Jeff Dean talks about how machine learning is being used at Google. After our first article covering the landscape of machine learning, and our second one bringing insights about TensorFlow and what's to come, we close our series with Jeff telling us about how machine learning is being used inside Google, what resources Google offers to developers and how you can get started with machine learning today. Where is it being used? JD: In our team, we've been building tools for solving machine-learning problems and then collaborating with lots of other teams over the last 5 or 6 years at Google to solve different machine-learning problems. And we started out collaborating with a handful of teams: the speech-recognition team, various teams that had computer-vision problems and then we built infrastructure software that allowed us to solve problems with those teams in machine learning.