Your browser is out of date

Update your browser to view this website correctly. Update my browser now


Gain insight from industry experts

Cloudera Now is a virtual discussion on how leading businesses are using machine learning to deliver business value across a whole set of industries and use cases.




12:00pm-1:00pm: Doors open

Take this opportunity to network with your colleagues, data experts, visit Cloudera & partner booths and also connect with Cloudera experts.

1:00pm-1:05pm: Introduction

Presented by Stephen Line, VP Northern EMEA at Cloudera

1:05pm-1:50pm: Machine Learning, Artificial Intelligence (AI), and the Future of Big Data Analytics

Presented by Amr Awadallah, PH.D. - Chief Technology Officer, Cloudera

Businesses and organisations of all sizes are suddenly flooded in data from a hyper-connected world and need solutions to analyse and act upon it. Machine Learning and Artificial Intelligence (AI) have emerged to help understand patterns in the data, predict outcomes, and prescribe the optimal actions to impact those outcomes. New use cases for machine learning are emerging every day – ranging from predictive maintenance and fraud detection, to product recommendation engines and proactive customer support. Gain first-hand experience into how some of the world’s best organisations are using data-driven insights to grow their businesses, operate more efficiently and manage risk.


1:50pm-2:20pm: Demystifying machine learning and AI - Cloudera

Presented by Hilary Mason, GM Machine Learning at Cloudera

It's an exciting time to be thinking about machine learning (ML) and artificial intelligence (AI), but it can be challenging to figure out a practical and reasonable approach to using these capabilities. Rapidly emerging technologies, disruptive economics, and a lack of best practices can leave you uncertain where to start or how to optimize an existing approach.

In this exclusive keynote, Hilary Mason, General Manager of Machine Learning at Cloudera, demystifies machine learning and artificial intelligence for the enterprise and discusses:

  • The history of machine learning 
  • Why now is the time to think about investing 
  • How to identify and execute on practical machine learning opportunities

2:20pm-2:35pm: Machine learning and deep learning at Perform Group/DAZN

2:35pm-2:50pm: Secure Machine Learning (for ourselves and our customers)

As we expand our use of machine learning in the enterprise to help us build faster, better products and engage our users in rewarding ways -- how are we securing these same models against attacks? Ongoing research in the machine learning community shows it can be trivial to extract customer data from models or fool neural networks into seeing a friendly face versus an attacker. In this talk, we'll cover some of the dangers and solutions for building better machine learning models to ensure the security of your product and the privacy of your customers.


Machine learning

2:50pm-3:20pm: Machine learning for IoT - Cloudera

Given the speed of change, rapid innovation, and the wide variety of standards and use cases that the Internet of Things (IoT) enables, organizations are increasingly looking at open source architectures for IoT in order to drive innovation and accelerate development.

Learn more about an end-to-end architecture for IoT based on open source and open standards. In this session we'll showcase an end-to-end architecture for IoT highlighting Eclipse Kura, which is an open source stack for gateways and the edge, and Eclipse Kapua, an open source IoT cloud platform.


3:20pm-3:50pm: Image Recognition for Online Quality Control - Data Reply

Visual object analysis is becoming more and more popular due to recent advances and availability of deep learning technologies. We present a use case we developed to automate tablet production quality control and save a producer millions of euros per year.

When a tablet press breaks, it begins to produce defective product that can take hours or sometimes days to recognise. With each manufacturing press producing hundreds of tablets per second, the monetary stakes of potentially losing days of product are high.

To address the issue, we prototyped a (deep) machine learning pipeline using transfer learning to detect defective tablets. In the presentation, we address some of the challenges we faced (avoiding false positives, impact of image noise, dust and unwanted reflections etc.). We also show how we used statistical pre-processing techniques to reduce the amount of data needed and improve the quality of the prediction.

3:50pm-4:20pm: Using machine learning for risk & fraud - Arcadia Data

When it comes to using machine learning for risk & fraud analytics it is critical to understand the end to end operationalisation of the workflow. In particular with FRTB, reporting analysis starts by developing models, training them, deploying in production, and reporting from BI tools built for Data Lakes. We'll review an end to end architecture for machine learning with risk models for FRTB using Cloudera's Data Science Workbench and Arcadia Data for visualisation.

Data science

2:50pm-3:20pm: Data science essentials - Bitesize

Data science, machine learning and artificial intelligence are the hottest topics in business today. Either augmenting existing services and functions or adding complete new capabilities, they promise value and opportunity from the one thing every organisation has in spades: data. However, according to IDC data from April 2017, only 15% of organisations in Western Europe are either using or piloting projects in this area.

Packed with customer examples and case studies, this event covers the fundamental points each organisation must consider before embarking on a journey into data science:

  • What are the limitations of current analytics approaches?

  • Why is now the right time to apply data science to business challenges?

  • Which use cases are best suited to delivering value?

  • What characteristics are needed in a platform for data science?

  • How does this fit with enterprise security and data governance?

3:20pm-3:50pm: Cloudera Data Science Workbench tech lab

Machine learning is all about the data, but it's often out of reach for analytics teams working at scale. Cloudera Data Science Workbench enables fast, easy, and secure self-service data science for the enterprise. In this session, Chris Royles, Principal Systems Engineer at Cloudera, will guide us through the Cloudera Data Science Workbench.

3:50pm-4:20pm: Build accurate predictive models on Cloudera with Automated Machine Learning - DataRobot

In this session, we will explore how to build, select and deploy accurate predictive models on your Cloudera environment, by leveraging the power of the world's leading Automated Machine Learning platform: DataRobot. 

Andre Balleyguier, Chief Data Scientist EMEA at DataRobot, will demonstrate how to deliver an end-to-end Machine Learning project, from business problem to model deployment. We will start from a dataset stored on Hadoop, then create a workflow to train, analyse and deploy dozens of models by leveraging the scalability of Apache Spark distributed computing as well as various open-sources tools (XGBoost, Tensorflow, Python, R, ...), within DataRobot's intuitive user interface.

DataRobot seamlessly integrates with the Cloudera Enterprise Platform and YARN; it enables business analysts, engineers and data scientists at various skills levels to deliver AI projects on Hadoop at scale, in a fraction of the time it takes with standard tools.

Professional services

2:50pm-3:20pm: Image management for computer vision

Rigorous improvement of an image recognition model often requires multiple iterations of eyeballing outliers, inspecting statistics of the output labels, then modifying and retraining the model. When testing data is present at the petabyte scale, the ability to seamlessly access all the images that have been assigned specific labels poses a technical challenge by itself.

Marton Balassi, Mirko Kämpf, and Jan Kunigk share a solution that automates the process of running the model on the testing data and populating an index of the labels so they become searchable. Images and labels are stored in HBase. The model is encapsulated in a PySpark program, while the images are indexed with Solr and can be accessed from a Hue dashboard.

3:20pm-3:50pm: The Vision of Data Science & Machine Learning - Cloudera Fast Forward Labs

The Machine Learning-Focused team, Cloudera Fast Forward Labs (CFFL), recently joined the company to support application of emerging ML research to Cloudera client use cases and to help deliver on the growth of Cloudera as an advanced analytics platform. From a vantage point at the intersection of academia, enterprise, and the startup world, the CFFL team takes on three roles in their work: Applied ML Research, Client Advising, and Building Products.

3:50pm-4:20pm: The value of Cloudera certification

Dan Johnson will be speaking about the value of certification for individuals, Cloudera partner companies and employers. He will be discussing the practicalities of registering for, and taking a Cloudera exam.


4:20pm - 4:30pm: Closing remarks

Presented by Stephen Line, VP Northern EMEA at Cloudera


Amr Awadallah, Ph.D. Global Chief Technology Officer
Hilary Mason General Manager, Machine Learning
Brian Goral Business Manager, Cloudera Fast Forward Labs
Christopher Royles Principal Systems Engineer, Cloudera
Wim Stoop Senior Technical Marketing Manager, Cloudera
Shant Hovsepian Co-founder and CTO, Arcadia Data
Katharine Jarmul Co-Founder at KIProtect
Brendan Moran Systems Engineering Manager, Cloudera
Alexander Loosley Technical Director, Data Reply


Your form submission has failed.

This may have been caused by one of the following:

  • Your request timed out
  • A plugin/browser extension blocked the submission. If you have an ad blocking plugin please disable it and close this message to reload the page.