With every organization digitizing its operations and taking advantage of data science tools, artificial intelligence, machine learning, the demand for professionals in their domain is always high. With machine learning being an important aspect of all automation tools, machine learning engineers are in the highest demand. According to Brandon Purell, Senior Analyst at Forrester Research, "one hundred percent of any company's future success depends on adopting machine learning. For companies to be successful in the age of the customer, they need to anticipate what customers want, and machine learning is absolutely essential for that." Let's understand why the demand for a machine learning engineer is more than ever.
This month Google revealed a major new approach to A.I. development that seems to call out to the most sensational and apocalyptic predictions in all of science fiction. Called "AutoML" for "auto-machine learning," it allows one A.I. to become the architect of another, and direct its development without the need for input from a human engineer. On the surface, that sounds like the sort of thing that could lead to the runaway evolution of the singularity, but it's actually Google's bid to put the incredible power of machine learning in the hands of ordinary humans. In essence, AutoML's strategy of using neural networks to design other neural networks is familiar; making programs to edit the code of other programs is the definition of machine learning. What makes AutoML new is how early into the process of designing a neural net it begins to intervene; AutoML doesn't just refine simple models that already exist, but selects those models in the first place, and then refines them on its own.
Qntfy is looking for a talented and highly motivated ML Engineer to join our team. ML Engineers are responsible for building systems at the crossroads of data science and distributed computing. You will do a little bit of everything: from tuning machine learning models, to profiling distributed applications, to writing highly scalable software. We use technologies like Kubernetes, Docker, Kafka, gRPC, and Spark. You aren't a DevOps, but an understanding of how the nuts and bolts of these systems fit together is helpful and you aren't a data scientist, but understanding how models work and are applied is just as important.
The role of the machine learning engineer has changed. In the past, a machine learning engineer was a software engineer with some knowledge of machine learning concepts. Today, a machine machine learning engineer is a software engineer who not only understands the latest machine learning and deep learning concepts but is able to deploy an AI system in production that is highly reliable, fast and scalable. In this course, you'll learn how to scale up your application and deploy them into production. By the end of the course, you'll have designed an ML/DL system, built a prototype and deployed a running application that can be accessed via API or web service.