Register here 👇 meetup.com/tech-webinars-by-…

Let’s talk about what developer productivity really means, which metrics are useful, and how to get a complete view of your engineering organization in one place. Join our webinar about How to Measure Developer Productivity? Register here: meetup.com/tech-webinars-by-…

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. 👇 meetup.com/tech-webinars-by-…

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 MLOps has become the biggest challenge for many teams and how chachaml helps close that gap for Clojure developers. 👇 flexiana.com/news/clojure/cl…

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 and growing the Clojure community • Chess, decision making, and software development • Agile methodology in Clojure projects • The role of testing and engineering decisions and more.. 🔗 Watch full episode👇 youtube.com/watch?v=lUFSpPWp…

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. For more 👇 flexiana.com/machine-learnin…

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. flexiana.com/machine-learnin…

Running machine learning in-house sounds powerful but is it the right move for your company? Check out this video where Jiri talks about a challenge many founders, CTOs, and product leaders face: when does it make sense to build machine learning internally, and how do you get started: youtube.com/watch?v=dz7-yiGD… Thinking about building machine learning solutions for your business? Check out our services👇 🔗 ai.flexiana.com/

Whether teams are automating tedious tasks within the organization or turning SaaS into something that thinks for itself depends on how closely the AI is linked to data and how the team works. For more 👇 flexiana.com/machine-learnin…

Three things make 2026 the year everything shifts for AI 👇 flexiana.com/machine-learnin…

This article examines six common bottlenecks and how to address them. Each section outlines the problem and solution, with examples from real projects. 👇 flexiana.com/machine-learnin…

Most ML projects fail before reaching production. The MLOps Maturity Model explains why- and what teams can actually do about it. Read more 👇 flexiana.com/machine-learnin…

Clojure corner 📣 We're joined by @obryant666, software engineer and Clojurist for a practical conversation about building software, frameworks, and recommender systems. 🎙️ Topics we cover: - Working with SQL and database structures - Database design: normalization vs. denormalization - A practical approach to Clojure development - Clojure vs. Haskell: freedom and flexibility and more… youtube.com/watch?v=kV4SZQM0… 👇

Join us for a practical discussion on AI vs. Human Teams and how work will be shared in 2026. 🔗Join here : meetup.com/tech-webinars-by-…

This article walks through both types of models and shows you what happens when you really pay attention to intent. The result? More conversions, higher margins, and pricing that just feels fairer all around. flexiana.com/news/2026/01/ba…

Join us for a practical discussion on AI vs. Human Teams and how work will be shared in 2026. This webinar focuses on how companies can introduce AI in a smart and safe way getting real value from automation while reducing risks. meetup.com/tech-webinars-by-…

In Series 3, we focus on causal inference using EconML from Microsoft Research. flexiana.com/news/2025/12/ma…

This is the second part of our Machine Learning in Clojure with libpython‑clj series. In this article, we focus on Bayesian Networks. We will show how to train them, run queries against them, and use them in Clojure. 👇 🔗 flexiana.com/news/2025/12/ma…

In this series, let’s walk you through training a model in @Python and integrating it right into your Clojure codebase. No hype, just straightforward steps to get machine learning running in #Clojure. 👇 flexiana.com/news/software-d…