Schedule

12.35 to 13.15

Psychology, AI & Big Data: the Science of Influence

14.50 to 15.30

Using Graphs for AI and ML with no BS

15.35 to 16.15

How to train your robot (with Deep Reinforcement Learning)

16.20 to 17.00

Delta Lake: Reliability and Data Quality for Data Lakes and Apache Spark

17.05 to 17.45

From trained models to machine learning systems

11.00 to 11.40

Simplify data analysis over the cloud

11.45 to 12.25

The case for a common Metadata Layer for Machine Learning

12.35 to 13.15

The present and the future of Cloudera

14.05 to 14.45

Transforming Transport: Mobility meets big data

14.50 to 15.30

Astronomical data analysis with Apache Spark

15.35 to 16.15

Recommendations in the real world

16.20 to 17.00

Natural Language Generation: Explaining the unexplainable AI?

17.05 to 17.45

Sales AI: Building and maintaining a knowledge graph

17.50 to 18.30

The future was this: Serverless face recognition in real-time video

11.00 to 11.40

Fix it before it Breaks: Incremental Learning for Predictive Maintenance

11.45 to 12.25

The end of the centralized era: Distribute or decentralize!

12.35 to 13.15

Time-Efficient Aircraft Fault Isolation Procedures with NLP techniques

13.20 to 14.00

IoT predictive maintenance for airplanes, bringing deep learning to the edge

14.05 to 14.45

Icaro project: Predicting Biosphere Behavior by using ML Cloud Engine

14.50 to 15.30

From euros to zero: using Probabilistic Data Structures to reduce costs

15.35 to 16.15

An Image is Worth a Thousand Words (Vol. 2)

16.20 to 17.00

Fairing: Bringing Kubernetes for Data Scientists

17.05 to 17.45

Operationalizing Data Science using the Azure stack

17.50 to 18.30

From Big Data to Artificial Intelligence. Descriptive Vs predictive

11.00 to 11.40

Solving Natural Language problems with scarce data

11.45 to 12.25

Detecting “things” with edge computing and the cloud, and all the stuff you don’t see

12.35 to 13.15

Context-Based Interpretability for Visual Attention using AI

13.20 to 14.00

Data Intelligence driving vehicle electrification forward

14.05 to 14.45

The delivery food algorithm to know your customers and improve their experience

15.35 to 16.15

#oneJourneyWiser

17.50 to 18.30

A public-owned IoT network built by citizens: The Things Network Madrid

10.55 to 11.35

How Charts Lie

14.40 to 15.20

How I won the Alibaba self-driving LIDAR point cloud segmentation competition

10.55 to 11.35

Purpose Built Databases: How to Build Modern Apps

11.40 to 12.20

Data Driven Dashboards with Kibana Canvas

12.25 to 13.05

Fighting Online Harassment with State-of-the-art Artificial Intelligence

13.10 to 13.50

AI and Medicine: From medical text, medical imaging… to genomics

13.55 to 14.35

Learning from 100M Users – The Evolution of Data Ingestion at Prezi

14.40 to 15.20

xAI for unsupervised ML in anomaly detection. Use case of LUCA Comms

15.25 to 16.05

Machine Learning for federated privacy-preserving scenarios

16.10 to 16.50

End-to-End ML pipelines with Beam, Flink, TensorFlow, and Hopsworks

10.55 to 11.35

Thoughts on IoT with some blockchain

11.40 to 12.20

Would you trust your model with your life? Research vs. reality in AI

12.25 to 13.05

Jupyter Notebooks on GCP (Development Best Practices/Tooling)

13.10 to 13.50

Spotting Voice Keywords and Beyond – Harnessing Audio Data in Deep Learning

13.55 to 14.35

Distributed Deep Learning with Keras/TensorFlow on Spark: yes you can!

14.40 to 15.20

Omni-Channel Customer-Centric Strategies in a Modern Architecture

15.25 to 16.05

The AI behind Alexa

10.55 to 11.35

Developing a cloud-based pure real time streaming platform and use cases

11.40 to 12.20

Core Analytics platform in a Cloud

14.40 to 15.20

Bayesian Voice Emotion Detection Applied to Robotics: Adding Uncertainty

16.10 to 16.50

AI-powered Automated Data Quality on Data Lakes