Michael Stefferson received his PhD in Physics from the University of Colorado before deciding to make the jump into machine learning (ML). He spent the last several years as a Machine Learning Engineer at Manifold, where he first started working on projects in the healthcare industry. Recently, Stefferson joined the team at Cerebral as a Staff Machine Learning Engineer and hopes to leverage data to make clinical improvements for patients that will improve their lives in meaningful ways.

A full-stack networking platform with machine learning, autonomous capabilities, and multicloud support allows devops engineers to focus on what matters most—building applications. The promise of digital transformation is enabling businesses to magnify competitive advantages, create new revenue streams, and improve customer experiences.

Machine learning has crossed the chasm. In 2020, McKinsey found that out of 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is predicted to deliver around $13 trillion. Before long, a good understanding of machine learning (ML) will be a central requirement in any technical strategy. The question is — what role is artificial intelligence (AI) going to play in engineering?

IT is now seen as integral to business rather than a cost center ripe for layoffs. Technology, people, and culture are worth protecting during economic contractions. A recent piece in Silicon Angle by Paul Gillin said out loud what I see firsthand: Cloud spending seems immune to budget reductions during contractions in the economy.

Operations staff get a hard time. The lowly systems administrator (sysadmin), database administrator (DBA) and all the other operations engineering team members from cyber penetration specialists to user acceptance testing (UAT) and so on are generally unloved.

Delivering business-critical applications and code relies on two key factors; functionality and efficiency. Mock and Unit tests are a few industry standards that aim to ensure the correct functionality of your code, catching potential bugs and issues before deployment. These tests are vital to workflows, CI/CD pipelines, and the overall build and deployment process. While functionality may be sound, one key aspect that is oft-forgotten is the efficiency and performance of your code.

The explosion of APIs, devices, applications, and data sources has complicated the task of building connectivity across the enterprise. As organizations are connecting to applications outside of their four walls, they risk becoming fragmented. Moreover, existing on-premise systems, such as AS/400 and ERPs, need to be able to communicate both internally and externally.

Artificial intelligence is already solving problems in all aspects of our lives, from animation filmmaking and tackling space exploration, to fast food recommendation systems that improve ordering efficiency. These real-world AI systems examples are just the beginning of what is possible in an AI Everywhere future and they are already testing the limits of compute power.

Instead of just opening up closed-off systems via APIs, many businesses are looking to use event-based triggers as well to react to changes in real time. Competitive pressures are driving the need for new thinking when it comes to developing applications that help a business react in real time. Being able to make real-time decisions based on events is at the heart of those efforts.