Where do you stand on this? Is data science part of IT and engineering, or is it still inside business reporting and analytics? And how do data scientist produce value with AI/ML?

#ai #ml #mlops #leadership #strategy

This is exactly why modern organizations bridge data science with software engineering, and ML engineers fill the technical gap, ensuring AI and ML power applications instead of getting stuck in notebooks or slides. 💻

❓ How much are data scientists expected to know about optimizing models for low-power devices?
🔧 Should they also deliver the entire backend system and cloud infrastructure for an AI application?
📱 What about firmwares, mobile app compatibility, and API scalability?
👀 And what about UX/UI?

💡 Data scientists build AI/ML models that power recommendation systems, forecast revenue, recognize physical activity, and much more. But these models only produce value when integrated into real-world applications, be it your smartwatch, a food delivery app, or an internal business process.

🛵 Your food delivery app has a recommendation system suggesting meals based on past orders, trending venues, etc. This is ML at work. Who delivers it?

⏱️Your smartwatch recognizes running, biking, or sleeping using ML trained on sensor data. How did this become a production-ready feature?

For years, data scientists were placed in analytics teams, producing reports but rarely working with engineers to deploy AI at scale. These silos still exist in many organizations. Is data science really just a reporting function? 🤔

Instead of seeing solving a bug as putting on a band-aid 🩹, I take it as an opportunity to reflect on the system's architecture and goals.

How do you approach debugging and software improvement in your work? 🤔

#softwareDevelopment #softwareDesign #softwareArchitecture #tech #systemsThinking

Beyond "why did this failure happen":

🎯Which part of the codebase allowed it?
📜Why is it coded that way?
👀Is it an unhandled corner case or a consequence of an upstream decision?
❓What should our system do when encountering this or a similar case?

The solution is often in the questions

Pointing out inconsistent or ambiguous abbreviations and var names might feel tyrannical, but it's truly about fostering clarity and quality in the codebase. Conceptual integrity ensures software is reliable, coherent, and maintainable: it's an architectural choice 🚀

#DeveloperVoices #TechWisdom

The read 📖 that caused this stitch: open.substack.com/pub/dataprod...

Of course CS specializations are still relevant, but a DS should know software design patterns, testing and CI/CD, and a SE should be able to tell supervised from unsupervised learning and to build simple models like linear regression or k-means. Their education does cover these things.

ML/DS/AI are effectively specializations within computer science. People building predictive models go through the same foundational courses as software devs. Why are some organizations not requiring from them the same level of proficiency in the basic concepts?

I've heard of DS siloed in business roles with no access to IT infra, and data teams not having any contacts with SE. Some org might be fine with DS not delivering anything but PoCs and offloading prod deployment to another team. But is "research" and "ad-hoc analysis" a sufficient output? 🧐

For modern data engineers or data scientists, who spend 80+% of their time coding, lacking basic software and devops skills simply means falling short at their job.

Thank you for this! I often find myself stressing that "inauthentic != bots". Earlier research also mostly focused on bots, but the landscape of inauthentic behavior is much broader and it’s important to call things by their precise names

Almost every influence operation account is run by actual people who craft their messages individually. They're still inauthentic accounts that are coordinating to manipulate the discussion on a platform in a desired direction or to influence people into thinking a certain way!

No doubt decluttering is therapeutic, and refactoring is decluttering applied to code (up to a certain scale, or it becomes more like rebuilding a house from scratch) 😊