Automating financial docs processing and merging: the Analyticshub & UbiOps

We would like to invite you to watch the next Use Case Webinar, together with William Groeneveld who built the “Analyticshub”.
Tailor-made automation scripts for you and your business, and available 24/7 for you to use. This could range from simple merging scripts to more advanced machine learning on your data and documents. The code runs on UbiOps, which takes care of serving and hosting the code.

In under 30 minutes, you’ll find out if the Analyticshub can save you time, and what value it can add to your business. Moreover, you’ll get a brief understanding of what UbiOps does in the background and how it can be applied to other use cases.

Webinars, hands-on workshops and podcasts!

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The latest #UbiOpsMonthly is live on youtube!
Watch it on-demand for the latest:

🌟1. Review of last month’s events:
– Webinar with BAM Energy Systems: how they use AI to optimize energy usage
– UbiOps Fundamentals Training with Ordina
🎯2. Upcoming features
🔎3. Tackling common pitfalls in UbiOps
📣4. Outlook coming 4 weeks
– Publication of Tableau Software integration.
– Mendix use case.
– Official release to production.
See you during the next Monthly on August the 12th!

Hoe zet BAM Energy Systems data en AI in om gebouwen te verduurzamen?

DUTCH ONLY! Pieter van der Mijle (data scientist) en Jaap Balvers (team lead analytics) van BAM Energy Systems vertellen hoe zij steeds efficiënter en slimmer het energieverbruik van hun klanten optimaliseren en daar advies over geven. Middels praktijkvoorbeelden, dashboards en architectuurplaten delen zij hoe ze steeds geavanceerdere modellen en analyses gebruiken om hun klanten verder te helpen, en wordt toegelicht welke rol UbiOps hierin speelt.

UbiOps Monthly: June 2021

🔥 1. UbiOps News 📣 2. Release news update – Request storage (batch and direct) – R client library – Model monitoring page (infra level) 🤝 3. Partners and Integrations update – Train, retrain, deploy and serve with UbiOps and Pachyderm Inc. – Deploy & monitor with UbiOps and Arthur 🙌  4. Upcoming features currently under development
See you during the next Monthly on July the 8th!

Week of the Fraud Investigator: June 2021

What should you know about your #AI project before starting on one?
Using practical examples Wouter Hollander and Mathyn Scheerder (from the Amsterdam Data Collective)  pointed out what to consider and where any pitfalls may be for each step of the #ML lifecycle.

Release Webinar: May 2021

During the session, Anouk Dutree, Product Owner at UbiOps, gave a demo of several new features, which we have recently added to our platform.
She demonstrated how to deploy R code in UbiOps, set up monitoring emails and more. In case you missed the live session, we got you covered with the recording of the demo.

Collision 2021 NL delegation – with UbiOps

We are excited to be one of the 14 tech start-ups to join the Dutch delegation to Collision Conf. 2021, (20-22 April). Special thanks to the Consulate General of the Netherlands in Toronto and Netherlands Enterprise Agency (RVO).

Introduction to MLOps and a model deployment tutorial

In this session, together with Riga Data Science Club, we reviewed important concepts and walk through a hands-on tutorial of an MLOps use case! We showed you how easy it is to deploy your model within 5 minutes on a production-grade (Kubernetes) cluster using UbiOps and make requests to it. 

UbiOps user event: AI ethics, AI audits and detecting deep fakes

On Thursday 11th of February from 14:00 to 16:00, we organized an online UbiOps User Event for customers, partners and our network. It’s invite-only to ensure high-quality discussions take place. The event is set up to inspire and share knowledge on themes that relate to UbiOps, such as AI ethics, AI audit, a deep fake detection use case and the future roadmap of UbiOps. 

Van algoritme tot schaalbare applicatie (in Dutch)

Hosts Jurjen Helmus and Lex Knape talk with our CEO Yannick Maltha about the story of UbiOps and the challenge for many organizations to bring data science projects from proof of concepts to live applications.
If you are working on a data science project and are curious about these challenges and the possibilities, then you should definitely listen to this podcast!

CTO at UbiOps Victor Pereboom gave a talk at the Data Science Salon

From algorithm to scalable application: How to bring data science beyond a proof-of-concept? Victor shared the origin story of UbiOps, and how we solved the challenge of transforming data science algorithms into scalable applications.

Data Science Lifecycle: step-by-step from model development to deployment

Together with Dr. Alexander Friedenberger, Educational Data Scientist at StackFuel, we will take you through the data science lifecycle, step-by-step. From development to deployment of the model. First, we focus on model experimentation using Jupyter notebook. After that, we export the model to UbiOps to deploy it within minutes.

Why is ML deployment important for data scientists?

During the online meeting we discussed the deployment of Machine Learning models, we looked at the goals of deployment technologies but we also familiarized you with game-changing method of implementing artificial intelligence algorithms and tell you why is it so different than conventional software.

AI in Asset Management

This time, we focused on AI in Asset and Property Management, with presentations from ground-breaking companies in the sector. UbiOps presented the client use-case with Asset Rail. Asset Rail wanted to monitor over 500 switches and more than flips of the Dutch railroads. They used time series data and different models, such as regression models and neural networks, but needed support from UbiOps to monitor and deploy their models quickly and effectively.

Deploying Machine Learning projects at scale with UbiOps by Dutch Analytics and

During this webinar, Thomas Cassou, the Director of Machine Learning platform from will tell you about some of their applications, the challenges his team faces, and some of the solutions and tradeoffs they have found to leverage machine learning in production.