Algolytics

We’ve just published the recording of our recent webinar on running machine learning in production.

If you’re working with ML in a real environment, you’ve probably seen that the hardest part isn’t building models - it’s deploying them, connecting them with data and decision logic, and keeping the whole setup reliable at scale.

That’s exactly what we covered during the session.

You can now watch the full recording here:
👉 https://eu1.hubs.ly/H0vlRqQ0

Most companies assume their address data is fine - and underestimate the real cost of inconsistencies and errors.

In Polish e‑commerce alone, up to 60% of addresses are entered outside the official addressing standard, generating hidden costs across logistics, marketing, analytics, and customer operations.

In our latest article, we explain:
◾ what address standardization includes and how to implement it in practice
◾ how address standardization differs from address validation and deduplication
◾ how poor address data leads to failed deliveries, duplicated CRM records, and unreliable analytics
◾ what are the most common address data issues
◾ why manual address standardization does not scale and when automation becomes necessary

If addresses are used across your systems, databases, or analytics = this guide helps you understand where address data quality issues originate and how to address them.

👉 Read the article: https://eu1.hubs.ly/H0tNvQV0

Jak podejmować decyzje w ciągu 5 ms od pojawienia się zdarzenia?

Na Warszawskich Dniach Informatyki pokazaliśmy, jak wdrożyć feature store, który przetwarza dane strumieniowe i dowodzi opóźnienie rzędu kilku milisekund.

To kluczowe dla firm, w których decyzje muszą zapadać w czasie rzeczywistym (real-time).

Krok po kroku przeszliśmy przez pełną architekturę rozwiązania, w której:
◾ profile i agregaty liczone są w czasie rzeczywistym (tumbling, sliding, real‑time windows)
◾ modele trenowane są na danych historycznych, a scoring działa bezpośrednio na strumieniu eventów
◾ AutoML optymalizuje złożoność obliczeniową modeli, a nie tylko ich jakość statystyczną
◾ decyzje podejmowane w milisekundach - przy dziesiątkach tysięcy zdarzeń na sekundę

Jeśli pracujesz w obszarach, gdzie milisekundy decydują o wyniku - takich jak fraud detection, real‑time bidding, IoT czy predictive maintenance - to wystąpienie jest dla Ciebie.

🎥 Zobacz nagranie i architekturę, którą pokazaliśmy na scenie WDI:
👉 https://eu1.hubs.ly/H0tNqc60

Many ML teams don’t fail because of models.

They fail because turning models into operable, scalable production applications is too slow, too complex, and too expensive.

Custom microservices, manual orchestration, and container‑heavy stacks make every change risky and every new ML use case harder to ship.

On April 28, we’ll show a production‑ready ML architecture where models, rules, and decision logic are composed into one maintainable workflow - with low‑code orchestration and an event‑driven runtime.

Not theory. Concrete end‑to‑end example: from training to batch and real‑time decisions.

📅 April 28 | 11:00–12:00 CEST
✅ Online & free
👉 Register for the webinar: https://eu1.hubs.ly/H0tQb2R0