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plan4all

This presentation was delivered as the closing keynote for the 2015 IoT Slam virtual conference. During the presentation, Ryft VP of Engineering, Pat McGarry, took a close look at how the IoT revolution is changing data analytics and driving the move of data analysis to the network’s edge where the data is being created. - See more at: http://www.ryft.com/blog/2015-iot-slam-keynote-harnessing-flood-of-iot-data-with-heterogenenous-computing-at-the-edge#sthash.x1Anoapb.dpuf

IoT Slam Keynote: Harnessing the Flood of Data with Heterogeneous Computing a...

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RTPI 2013 Dominik Grogler

Russell Publishing

In this talk, we will present how we tied Python together with Databricks and MLflow to productionalize a machine learning pipeline. Through the deployment of a fairly standard classification model, we will present what a machine learning pipeline in Production could look like. The project consists of two pipelines; training and prediction. We are using the S3 Bucket as a source of data. The training pipeline trains various models on data, registers them in Mlflow, and stores all metrics and hyperparameters. Using Grid Search, the best model is chosen and moved to the Production Stage in MLflow. The Production model can then be deployed using Flask, or just a UDF if we want to process data in a batch. The prediction pipeline will then use the deployed model to make a prediction, whether on-demand or in a batch.

ML Production Pipelines: A Classification Model

Databricks

• Maurice is CEO of Nathean Technologies, a software company specialising in the development of data visualisation and analysis products. He has held senior technical and executive positions over the past 20 years in the software industry. • He has consulted for government bodies and blue-chip private sector clients in Ireland, the UK, US and Australia. Maurice graduated with a B.Sc. (Computer Applications) from Dublin City University in 1991. • In 2011 Maurice participated in the Enterprise Ireland Leader4Group Programme in Stanford University, a programme focused on strategic leadership. • He currently serves on the Steering Board for the Centre for Advanced Data Analytics Research (CeADAR) Technology Centre in UCD. Maurice’s presentation focuses on the advances in in-memory data analysis and visualisation and the impact they are having on data warehouses.

Agile v Warehouse? Maurice Lynch CEO of Nathaen Technologies - Dublinked Data...

Dublinked .

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ATMOSPHERE .

DMA Ignite Night - Status DMA

Data Market Austria

• Dr. Ingo Keck graduated in physics at the University of Regensburg, Germany, and holds a doctor europeus in computer science from the University of Granada, Spain. He is an internationally recognised researcher in the field of advanced data analytics and statistics, has years of experience in writing grant proposals and was leader of various academic open source projects. • After working as researcher, professor and lecturer in Spain and Germany he recently joined the Centre for Advanced Data Analytics Research (CeADAR), DIT, Ireland and has authored over 45 peer-reviewed publications and specialises in computational intelligence and machine learning. • Since 2013 Dr Ingo Keck has also been involved in the Open Government Partnership and the Open Data movement in Ireland. His presentation is on Advanced Data Analytics and Open Data.

Advanced Data Analytics and Open Data - Dr Ingo Keck of CeADAR - Dublinked Da...

Dublinked .

Knowage official presentation 2018

KNOWAGE

85% of enterprises have a digital transformation strategy in place, but only 30% have really execute on it. What about you? In this session, we will explore how enterprises can embark on their digital transformation journey, leveraging APIs and a service-based architecture. Isabelle shares how several customers have achieved their business goals and describes the technical approach they have taken to do so.

WSO2Con ASIA 2016: API Driven Innovation Within the Enterprise

WSO2

KNIME Data Science Learnathon: From Raw Data To Deployment

KNIMESlides

The edge computing market today includes consumer apps and devices, and the industrial sector, where increasingly powerful CPUs drive everything from wind turbines to autonomous vehicles, robots, drones and equipment. The device market is growing explosively: These devices gather a wealth of data from a broad array of sensors – and have the potential to optimize efficiency, safety and performance, and revolutionize productivity and user experiences. But to deliver these benefits they need to become truly smart, performing analysis, training and inference on high volumes of sensor data on-the-fly. There is an urgent need for software that simplifies and automates data analysis and inference at the edge, helping devices and systems learn from and make predictions about their environment: Cameras that recognize and track their targets; self-driving cars that choose the least congested routes using real- time predictions for intersections ahead; and drones that dynamically swarm, find their targets and gather intelligence without human oversight. These examples require each device to make decisions based on a real-time analysis of its own sensor data fused with the analysis and predictions from other systems: Drones in a swarm need to collaborate or they will collide; they must gossip their insights to each other to enable the swarm to perform effectively. Today, the software to enable each of these complex scenarios must be developed from scratch, starting with raw data feeds and network protocols. To unlock the potential of an edge environment rich in sensors and power-efficient computing platforms developers need a simple way to get from vast amounts of raw data to insights and predictions. What's needed is a new Architecture for the intelligent edge – one that consumes raw data from devices at the edge, and automatically creates a “digital twin” for each real-world system from its data. Digital twins statefully process their own data at the edge, analyzing, learning and predicting in real-time. Digital twins can find anomalies or correlations in their own data, and self-train powerful neural network models that enable them to predict their future performance, then share semantically enriched insights with other digital twins to solve system problems. The architecture helps application developers by dynamically creating digital twins that learn from their own data – automatically building a model of the real world that is always up to date, executes in real-time, and makes accurate predictions of the behavior of complex systems.

Edge intelligence

Rakuten Group, Inc.

OpenPOWER partner presentation - GTS Data

Ganesan Narayanasamy

20150630 kca big-data-with-cloud_output

ericpi Bi

ELT is a fundamentally better way to load and transform your data. It’s faster. It’s more efficient. And Matillion’s browser-based interface makes it easier than ever to work with your data. You’re using data to improve your world: shouldn’t the tools you use return the favor? In this webinar: - Explore the differences between ELT and ETL - Learn why ELT is a better, more modern process - Discover the latest trends in ELT and how they apply to your business - Find out how Matillion ETL makes loading large amounts of data easier

ELT vs. ETL - How they’re different and why it matters

Matillion

Volvo Group Connected Solutions: Starting the Observability Journey with Elastic

Elasticsearch

Watch full webinar here: https://bit.ly/3frkTmj When you hear data virtualization, do you think BI and analytics? If so, you’re misinformed and missing out on a whole set of possibilities and capabilities of this technology. It is probably also why you think data virtualization is of no use to you if you need to access your data through APIs. This is why we’re back with another episode of Myth Busters! We enter the world, or ecosystem, of APIs and API-based architectures to investigate whether data virtualization plays a role in it. Maybe we’ll even learn that it possibly can enhance API capabilities and increase the benefits? Here’s what we’ll be exploring: - Is there a place for data virtualization in an API strategy? - Can data virtualization enhance the deployment and exposure of APIs? - Can data virtualization work as a service container or as an API gateway? - Data virtualization and GraphQL...are they really like oil and water?

Myth Busters IV: I Access My Data Through APIs–Data Virtualization Can't Do This

Denodo

Interoute information

Mak Khosse

Introduction to Redis Data Structures

ScaleGrid.io

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