Algolytics

Algolytics helps automate business decisions and processes with Machine Learning, AI & Data Science.

[Algolytics @algolytics]

Watch the full webinar: https://t.co/IEdjD8hLSY

[Algolytics @algolytics]

A model going to production usually passes through several different roles: a data engineer, a DevOps engineer, an MLOps engineer, a data scientist, and often more. Each works in a different tool, and those tools barely connect.

The barrier is rarely the model itself. It's everything around it: ETL pipelines, container deployments, security, network, installation, infrastructure. Every role adds a handoff, and every handoff adds time before anything runs in production.

This clip from our webinar shows where that effort goes, and why a set of disconnected tools slows ML delivery.

https://t.co/m4q3VhM3EQ, our low-code MLOps layer, brings these functions into one platform. There's no separate DevOps queue and no manual security or installation step. One person who knows the business goal can build the model, handle feature engineering and integration, and deploy it to production.

Fewer roles and fewer handoffs mean a smaller team and a faster path from idea to a working model.

▶️ Watch the full breakdown in the webinar. Link to the full webinar in the first comment.

#mlops #machinelearning #datascience #modeldeployment #lowcode

[Algolytics @algolytics]

Read the article: https://t.co/9bI9tAEQCe

[Algolytics @algolytics]

Can you store a geocoded address and reuse it - or do you pay for the same lookup twice? We tested 10 standardization and geocoding services to find out.  

Google limits caching of coordinates to 30 days. Mapbox lets you store only results marked Permanent, not Temporary. If you can't store the geocode, every reuse is another API call - a per-lookup cost, and a process that fails when the provider's service is down.

The second issue is whether the address can be matched across systems at all - and that comes down to TERYT (Poland's official territorial register, unique nationwide and independent of any vendor). Only Data Quality and Locit return TERYT identifiers; the other 8 services keep partial name matching you can't reliably join on.

🔍 See how all 10 services handle storage, TERYT and point identifiers before you sign. Link to the article in the first comment.

#geocoding #dataquality #locationintelligence #dataintegration #teryt

[Algolytics @algolytics]

Register for the webinar: https://t.co/XJsVhWSCrW

[Algolytics @algolytics]

Watch a click on your site turn into a live purchase-probability prediction, served through an API while the visitor is still on the page. In this 60-minute webinar we walk through a working case - how we built it and how it runs.

It runs on data you own - your own site events - not on audiences you rent from platforms like Google, Meta, or Amazon. The profile, the model, and the prediction stay yours.

You act on that prediction in the same session: a recommendation, an offer, or a change in what the page shows next.

We'll also show what decides whether this holds up in production: keeping the profile accurate to the millisecond and the API fast under peak traffic.

Marcin Woch, our CEO, walks through it end to end. For teams running high-traffic stores, marketplaces, or subscription sites - the ones that don't want to depend on third-party cookies or walled gardens.

🗓️ Wednesday, 22 July, 11:00 (CEST) · online · free
📥 See a full real-time case walked through live, from tracker to prediction. Registration link in the first comment.

#realtimemarketing #personalization #firstpartydata #ecommerce #conversion

[Algolytics @algolytics]

Read the article: https://t.co/YQO4gBLu7Q

[Algolytics @algolytics]

When a geocoding service stamps an address with its highest quality label, how often is that address actually correct?

We tested 10 services on 2,500 real Polish addresses. For most providers, fewer than half of the records carrying the top label had fully correct standardization.

This matters because a wrong record stamped high quality skips every check, so it surfaces later, at a more expensive point. For example, a wrong postal code that routes a parcel to the wrong carrier.

Across 100,000 shipments a year, the false-positive errors alone cost about PLN 18,000 for the best service and about PLN 327,000 for the weakest.

📄 Read the full article. Link in the first comment.

#geocoding #dataquality #addressdata #locationintelligence #logistics

[Algolytics @algolytics]

Register for the webinar: https://t.co/XJsVhWSCrW

[Algolytics @algolytics]

You can personalize in real time using data you fully own - without renting audiences from Google, Meta, or the big marketplaces.

Those platforms hand you less data on who you're reaching and charge more for it - Meta CPMs are up around 20% year over year, while third-party signals keep eroding across browsers. The usual fixes - stitching tools together or building real-time infrastructure in-house - are expensive.

This free 60-minute webinar walks through the alternative, end to end.

You'll learn how to capture what someone does on your site and app, turn it into a live customer profile, build an ML model on it, and act on its prediction while they're still on the page.

You'll also see what makes this hard in practice: large event volumes, keeping a profile current to the millisecond, and keeping features consistent between training and production.

Marcin Woch, our CEO, walks through it and builds the whole thing live. For teams running their own high-traffic store, marketplace, or subscription site.

🗓️ Wednesday, 22 July, 11:00 (CEST) · online · free
📥 Sign up and watch it built live, from tracker to real-time prediction. Link in the first comment.

#firstpartydata #realtimemarketing #personalization #featurestore #mlops

[Algolytics @algolytics]

Download the full report: https://t.co/St38F1X7LO

[Algolytics @algolytics]

In our new report, we ranked 10 standardization and geocoding services on 2,500 real courier addresses, and the global names came in behind local ones.

The score weighs three things at once: standardization (is the address usable for matching records), geocoding (location accuracy), and trust (how often service's own top-ranked result is actually wrong).

What are the main conclusions?
◾ One service scores above http://0.9: Data Quality, at http://0.94. Built for the Polish registers, it leads on all three indexes at once, so most records can be auto-processed without manual checks.
◾ Esri (http://0.64) and Google (http://0.62) geocode very well but are weaker elsewhere: Esri on standardization and trust, Google on trust, mostly on rural addresses.  
◾ HERE (http://0.55), TomTom (http://0.51), and Azure (http://0.51) geocode acceptably, but even their best-rated results can be off by a few hundred meters, so it's worth adding a few safeguards before you rely on their label.
◾ Emapa, Precisely, and Mapbox score lowest.

Download the full report and the cost-simulation Excel - link in the first comment.

#geocoding #dataquality #locationintelligence #logistics #benchmark

[Algolytics @algolytics]

Download the report: https://t.co/St38F1X7LO

[Algolytics @algolytics]

We've just published a report comparing 10 standardization and geocoding services on the same 2,500 real courier addresses, measured against a reference set consistent with TERYT, EMUiA, and PNA.

It is the first time we put these tools head-to-head on the same real data, instead of comparing vendor claims.

We score each service (Azure, Here, TomTom...) on three things. Standardization: is the address clean enough to match records reliably. Geocoding: how accurate the location is. Trust: how often a "high quality" result is wrong.

We also ran a cost simulation: depending on which tool you choose, the extra cost can differ by almost PLN 800,000 a year per 100,000 shipments, before the tool's own price. You can rerun it on your own volume in the Excel we built.

The costliest error is not a missing match. It is a confident-looking wrong one that passes automation and surfaces later, where the fix costs more.

Link to the full report and the Excel in the first comment.

#dataquality #geocoding #locationintelligence #logistics #datascience

[Algolytics @algolytics]

Most AutoML tools pick the model with the best accuracy and stop there. I think that misses half the problem. 

Two candidate models can have almost the same predictive power, but very different execution costs in production. One is lean. The other runs heavier on every inference call. 

Our platform builds a penalty for computational complexity into the selection algorithm. So the search doesn't just rank by accuracy. It actively eliminates models that are expensive to run. 

What I find useful in practice: the algorithm usually lands on a simpler model that's nearly as good as the top performer. We trade a fraction of a percent in accuracy for a real cut in inference cost. At high request volumes, that's the difference that matters. 

In the full webinar I show what that cost gap looks like under real load, measured on a deployed system. 

Watch the full webinar: https://t.co/VZM6cn7Xu3

#automl #mlops #machinelearning #modeloptimization #datascience

[Algolytics @algolytics]

Most ML teams don't fail at training models. They fail at everything that comes after. 

Integration, real-time feature pipelines, business rules, drift monitoring - that's where projects stall.  

We published an article that breaks down what a production ML application actually consists of, where the bottleneck sits, and how integrated MLOps architecture changes the economics.  

It includes a benchmark where one competing platform came in 20× more expensive for comparable accuracy, and two end-to-end demos built on real data. 

Read the article here: https://t.co/WlJybWY6AJ

#mlops #machinelearning #datascience #aiplatform #productionml

[Algolytics @algolytics]

Putting an ML model into production requires a system where all parts work together end to end.

The problem appears when each part is built separately.

What works better is one coherent flow, where:
◾ models are generated efficiently and prepared for use in production
◾ data can be consumed from multiple sources such as databases, APIs, and brokers
◾ models and business rules are executed together as one production scenario
◾ deployment and orchestration are handled in the same layer
◾ streaming data can be processed and features updated continuously on top of incoming data

Instead of connecting all of this manually, the whole process runs within one architecture.

Fewer components. Less integration overhead.

If you want to see how it works in practice:
👉 watch the full talk: https://t.co/CNWPp8wwBp

#machinelearning #mlops #datascience #ai #modeldeployment #streamingdata #dataplatform

[Algolytics @algolytics]

Adresy są dynamiczne. Bez regularnych aktualizacji szybko przestają być poprawne.

Dobrze prezentuje to nasza aktualizacja baz adresowych za I kwartał 2026 roku.

◾ +42 710 nowych adresów w bazie budynków
◾ +1 192 nowych ulic
◾ +215 nowych nazw ulic
◾ 4 zmiany nazw ulic
◾ 39 usuniętych nazw ulic
◾ +4 nowe kody pocztowe, 2 usunięte

To tysiące zmian, które - jeśli nie zostaną uwzględnione - negatywnie wpływają na efektywność procesów logistycznych, prac terenowych i innych procesów opartych o adresy.

Dlatego nasze bazy adresowe aktualizujemy kwartalnie.

Chcesz sprawdzić ich jakość?

Pobierz darmową próbkę: https://t.co/N04ZQH9m5b

#dataquality #addressdata #datamanagement #geocoding #TERYT #dataengineering

[Algolytics @algolytics]

Training a model is no longer the hardest part - if you have data and a basic stack, it’s manageable.

The real issues start when the model has to work in production:
◾ integration with multiple data sources
◾ large-scale data processing
◾ strict latency requirements
◾ ongoing changes and maintenance
◾ high effort for every update

Plus one more thing we often see: too many disconnected tools across the pipeline.

This slows everything down and extends time-to-production.

We break down these challenges and how to handle them in the full talk.
Watch the full talk here: https://t.co/JH2wBT6IcF

#mlops #machinelearning #datascience #ai #deployment #scalability

[Algolytics @algolytics]

The same ML use case can cost 20x more - depending on how it’s built.

We tested a typical production setup: AutoML models, ~150 queries per second.

Model accuracy was similar across platforms.
Cost wasn’t - in some cases over 20x higher.

Where does that difference come from?
◾ models with similar accuracy can have very different inference cost
◾ scoring can run through heavy runtimes and libraries - or as simple compiled code
◾ architecture - containerized Python workers and orchestration overhead vs JVM-based, event-driven execution with Vert.x

At this scale, these differences repeat hundreds of times per second - and that’s what drives TCO.

Watch the full recording: https://t.co/AgBkxUxu7e

#mlops #machinelearning #datascience #scoring #costoptimization #ai #algolytics