Join us for a practical discussion on how to make better engineering decisions with the right data and create an environment where teams can do their best work. 👇
Flexiana
@flexiana
We are The Service Builders. A team of senior designers, developers and agile coaches. @jiriknesl is our CEO.
- This is the first article in our three-part series, Clojure Meets Production MLOps: How chachaml Delivers AI-Native Workflows. In this article, we look at a problem many ML teams run into. Building a model is one thing. Running it reliably in production is another. We cover why
- New episode of Clojure Corner 📣 In today’s episode, we’re joined by Timothy Pratley — Clojure advocate, educator, and contributor to the Clojure ecosystem. We dive into a wide range of topics, including: • Timothy’s programming background and journey into Clojure • Teaching
- Machine learning models get the spotlight, but the real impact lies upstream. The majority of success—often as much as 80%—comes from how well data is prepared, cleaned, and delivered. Strong data pipelines directly translate into more accurate, reliable, and efficient systems.
- Many teams build models that perform well in tests. But without the right setup around them, those models stay in notebooks or demo apps. They never become part of a real product. This is where MLOps matters.
