Data & AI

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Nov 02

No sessions scheduled.

Nov 04

11:30

RAPIDS – Workshop GPU Accelerated Data Science

Ahmet Erdem
RAPIDS is an open-source software that accelerates the whole Data Science Pipeline from Data Preparation/Visualization to Machine Learning on GPU. In this workshop, you get familiar with RAPIDS which runs on Python with pandas/scikit-learn-like API and you experience the speed-up it provides.
Technologies: RAPIDS
Topics: Data & AI
14:00

Data Science in Production – Bonus Box at Ahold Delhaize

Suzanne Boer, Roel Bertens
When moving a Data Science project from proof of concept to production many new challenges arise. Today we discuss how to steer your product towards value based on experience from developing the Bonus Box at Ahold Delhaize.
Technologies:
Topics: Data & AI
15:30

The Quantity of Quality: Image Recognition Lessons from the Flower Auction

Dirk Guijt, Vadim Nelidov
As more and more businesses digitalize, physical products often become represented by their photographs. While advantageous in a lot of ways, this also presents businesses with new kinds of challenges. How to ensure that these materials are of sufficient quality? How to measure image quality use it to motivate users to improve? What visual factors make the product more appealing and sellable? We will consider these and many related questions in this session with the example of the world’s biggest flower auction. It will combine a range of business insights with technical showcases in the domain of Data Science. Key words: Data Science, Image recognition, Image quality, Neural Networks, Ranking algorithms, Quality evaluation, Digitalisation
Technologies: AWS
Topics: Data & AI
18:00

Google Data mesh and streaming data ingestion

Navin Goel
Technologies: Google Cloud
Topics: Data & AI
19:00

Google Vertex AI

Wouter Roosenburg
Vertex AI is a unified MLOps platform to help data scientists/ML engineers increase experimentation, deploy faster, and manage models with confidence. Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. One AI platform, every ML tool you need A unified UI for the entire ML workflow Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI. Pre-trained APIs for vision, video, natural language, and more Easily infuse vision, video, translation, and natural language ML into existing applications or build entirely new intelligent applications across a broad range of use cases (including Translation and Speech to Text). AutoML enables developers to train high-quality models specific to their business needs with minimal ML expertise or effort. With central managed registry for all datasets across data types (vision, natural language, and tabular). End-to-end integration for data and AI You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI for seamless integration across the data-to-AI life cycle. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Support for all open source frameworks Vertex AI integrates with widely used open source frameworks such as TensorFlow, PyTorch, and scikit-learn, along with supporting all ML frameworks via custom containers for training and prediction.
Technologies: Google Cloud
Topics: Data & AI

Nov 05

No sessions scheduled.