Farewell 2020, it has been a year for the books. All the more reason to make note of things.

Here are my 2020 spark-notes, this includes things I’ve learned about work and life, thoughts I’ve had, habits I’ve formed, products I’ve used, podcasts I’ve listened to, things I’ve read, and more.

I enjoy when people share guidelines and learnings that you can cross reference with your own experiences and learnings. You’re only reading what has worked or not worked for me and shouldn’t be taken as an instruction manual for your own life or company.

I hope that you will find a golden nugget or two in here that will help you level up your 2021 and beyond.

The past ten months have felt like a couple of years – so I’ve decided to stop, reflect, and share some of my learnings.

I’ve built up two companies; one from the ground up regarding technology and infrastructure alone, and the other from absolutely nothing. One of those companies has since closed, and the other is still puttering along in my free time.

I made a tough decision to commit my startup journey to moonlighting hours only and find a more stable company to join that’s just as exciting and could help me grow my skills and network. It was time to start a job search.

I’d like to share some of my thoughts and experiences specifically around this job search; what went well, what didn’t go well, warning signs, and when the universe slaps you in the face with something great.

The rise in usage of cloud computing resources and container management platforms for executing AI (Artificial Intelligence) and ML (Machine Learning) workloads has led many engineers and companies to question the suitability and effectiveness of Kubernetes' resource management and scheduling to meet the growing requirements of these workloads.

So why’s that? What patterns, architectures, and procedures has led these companies and engineers to this problem of scaling ML platforms on Kubernetes? And what kind of solution could we apply to help solve those problems?