Automation has improved productivity across entire sectors. Software has driven much of this automation, but many workflows still require decisions by humans.
The promise of machine learning is to automate the decision-making process by training algorithms, based on empirical evidence. That promise is becoming very real and tangible for developers who are now able to leverage massive amounts of data with cloud computing power via learning libraries like TensorFlow and frameworks like MXNet.
This track focuses on how software engineers today can take advantage of these modern learning methods. We’ll discuss the practicalities and pitfalls to avoid when automating decisions with talks from thought leaders in the field and real-world practitioners building apps leveraging machine learning.