I am still working on the Kubernetes stack behind my personal website whenever I have some free time. The goal is still the same – to build my own personal Kubernetes-powered data science/machine learning production deployment stack (And yes, I know about Kubeflow/AWS Sagemaker/Databricks/etc). However my key objective now lies not with finding out whether… Continue reading Onto Kubernetes – Part 3
Tag: MLOps
Onto Kubernetes – Part 2
About 2 months ago, I started migrating my entire personal stack onto Kubernetes from regular virtual servers. So what has happened in the meantime? Have I freed up more operation maintenance time to do more interesting data science development work yet? Unfortunately the answer is no, at least for now. It turns out that migrating… Continue reading Onto Kubernetes – Part 2
Technical Debt vs. Mortgage: A Data Science Homeowner’s Guide
(I used chatGPT to help me make the written content more “engaging” and “LinkedIn-like”, so keeping the 2 versions below for comparison purpose.) [ChatGPT rewritten version] Building a minimal viable product (MVP) in data science is like buying your first home with the maximum mortgage. It’s often necessary to move quickly and show business value… Continue reading Technical Debt vs. Mortgage: A Data Science Homeowner’s Guide
Short review of Designing Machine Learning System
I have finally finished “Designing Machine Learning Systems” after a few weekends of focus reading. It is one of the rare technical books that I finished in its entirety, and I thoroughly enjoyed it. Just to offer a quick book review below. The book is amazing in the following aspects: ✅Provides a high-level overview of… Continue reading Short review of Designing Machine Learning System
ML system design – problem definition & consulting
I recently started reading the excellent book called Designing Machine Learning Systems by Chip Huyen. In the first few chapters, the book illustrated very clearly the differing stakeholder expectations of an ML system, by using a restaurant recommendation app as an example. Data scientists / ML engineers ➡️ Want a model that recommends the best… Continue reading ML system design – problem definition & consulting