Machine Learning Logistics

by Ted Dunning and Ellen Friedman

Lessons Learned

The shape of the computing world has changed dramatically in the past few years with a dramatic emergence of machine learning as a tool to do new and exciting things. Revolution is in the air. Software engineers who might once have scoffed at the idea that they would ever build sophisticated machine learning systems are now doing just that. Look at Ian with his TensorChicken system. There are lots more Ians out there who haven’t yet started on that journey, but who soon will.

The question isn’t whether these techniques are taking off. The question is how well prepared you will be when you need to build one of these systems.

New Frontier

Machine learning, at scale, in practical business settings, is a new frontier, and it requires some rethinking of previously accepted methods of development, social structures, and frameworks. The emergence of the concept of DataOps—adding data science and data engineering skills to a DevOps approach—shows how team structure and communication change to meet the new frontier life. The rendezvous architecture is an example of the technical frameworks that are emerging to make it easier to manage machine learning logistics.

The old lessons and methods are still good, but they need to be updated to deal with the differences between effective machine learning applications and previous kinds of applications.

We have described a new approach that makes it easier to develop and deploy models, offers better model evaluation, and improves ability to respond.

Where We Go from Here

Currently, machine learning systems are beginning to be able to do many cognitive tasks that humans can do, as long as those tasks are ones that humans can do at a glance and as long as sufficient training data is available.

One emerging trend is to use deep learning to build a base model from very large amounts of unlabeled data, or even labeled examples for some generic task. This base model can be refined by retraining with a relatively small number of examples that are labeled for your specific task. This semi-supervised kind of learning, together with the distribution of base models, is going to make it vastly easier to apply advanced machine learning to practical problems with only a few examples for training. As this approach becomes more prevalent, the entry costs of building complex machine learning systems are going to drop. That drop is, in turn, going to cause an even larger stampede of people jumping into machine learning to build new systems.

Beyond these semi-supervised systems, we see huge advances in reinforcement learning. These systems are working on very hard problems and aren’t working nearly as well (yet) as the newly available image and speech understanding systems. The promise, however, is huge. Reinforcement learning holds a key to helping computers truly interact with the real world. Whether advances in reinforcement learning will happen over the next few years at the pace of other recent advances is an open question. If the pace stays the same, the revolution we have seen so far is going to seem miniscule in a few years.

We can’t wait to see what happens. One thing that we do know from experience is that it will be the logistics, not the learning, that will be the key to make the next generation of advances truly valuable.