Deep Learning Training vs. Inference: What’s the Difference?

Machine learning inference is the ability of a system to make predictions from novel data. This can be helpful if you need to process large amounts of newly accumulated information from a widely dispersed IoT network, for example.

Here’s a simple analogy: Say you’re teaching a child to sort different fruits by color. You might first show them a tomato, an apple, and a cherry to learn that those fruits are red. Later, when you show them a strawberry for the first time, they’ll be able to infer that that fruit is red, too—since it’s similar in color to the apple, tomato, and cherry.

There are three key components needed for machine learning inference: a data source, a machine learning system to process the data, and a data destination.

Sometimes a data source may actually be multiple sources accumulating information from several places. Such is the case when information is captured from an array of IoT inputs. A machine learning model employs a series of mathematical algorithms to sort and analyze live data as it comes in. Those algorithms can make deductions based on features in data—qualities that differentiate one item from another. The model then “scores” these data points and gives an output based on how it was trained to interpret the data. Those output scores can help determine the next course of action. 

For example, let’s say you’re collecting data from a customer service chatbot on an e-commerce site. Your data source would be the customer input from the online chat platform. Then, a machine learning model could determine that, if a customer writes that they are “dissatisfied” with a product, the customer may want a refund. A separate system could then help issue a refund for that customer.

During deep learning training, a DNN sifts through preexisting data and draws conclusions based on what it “thinks” the data represents. Every time it comes to an incorrect conclusion, that result is fed back to the system so that it “learns” from its mistake. 

This process makes connections between the artificial neurons stronger over time and increases the likelihood that the system will make accurate predictions in the future. As it’s presented with novel data, the DNN should be able to categorize and analyze new and possibly more complex information. Ultimately, it will continue to learn from its encounters and become more intuitive over time.

Deep learning inference can’t happen without training. The difference between deep learning training vs. inference in machine learning is that the former is a building block for the latter.  

Think about how humans learn to do a task—by studying how it’s done, either through reading a manual, watching how-to videos, or observing another person as they complete the task. We learn to mimic others in the same way that DNNs pick up on patterns and make inferences from existing data.

Deep learning can be utilized in a wide variety of industries—from healthcare to aerospace engineering. But it’s especially helpful in situations where a machine needs to make multiple decisions in real time, such as with autonomous vehicles.

Say you’re training a driverless vehicle to obey traffic laws. The vehicle’s system would need to know the rules of traffic first. So a programmer would train it on datasets that help it learn to recognize traffic signs, stoplights, and pedestrians, among other things. It would memorize the rules of the road by looking at examples and learning to analyze what it should do when it observes certain signs or scenarios.

But when driving on the road, the vehicle would need to make inferences based on its surroundings. It sounds simple to train the vehicle to stop at a red stop sign, but what happens if a stop sign in the real world has a different font or is a slightly different shade of red than the ones in the data set? 

This is where deep learning inference comes in—it helps machines bridge the gap between the data they were trained on and ambiguous information in the real world. The vehicle could infer that the sign it came across is still a stop sign, thanks to a DNN that can extrapolate from features it recognizes.