TCP1P.net Meetup Vision, Objectives and Roadmap

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Slides for my talk at Cloud Foundry Summit Europe 2016. Nearly 1.2 million people die in road crashes each year (WHO - 2015) with additional millions becoming injured or disabled. One big part of this problem is worst road traffic conditions and unless action is taken, road traffic injuries are predicted to become the fifth leading cause of death by 2030. Moreover, although road traffic injuries have been a major cause of mortality for many years, most traffic accidents are both predictable and preventable. In this talk, we want to demonstrate a scalable IoT platform that uses weather data and data from other cars to warn drivers of dangerous conditions. We will show how CF can help to save human lives and the architecture behind this. Additionally, we will also explain the data science that is involved.

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Notebooks @ Netflix: From analytics to engineering with Jupyter notebooks

Michelle Ufford

Slides from JupyterCon 2018 in NYC on 8/23/2018. Notebooks have moved beyond a niche solution at Netflix; they are now the critical path for how everyone runs jobs against the company’s data platform. From creating original content to delivering bufferless streaming, Netflix relies on notebooks to inform decisions and fuel experiments across the company. Netflix also uses notebooks to power its machine learning infrastructure and run over 150,000 jobs against its 100 PB cloud-based data warehouse every day. The goal is to deliver a compelling notebooks experience that simplifies end-to-end workflows for every type of user. To enable this, Netflix is investing deeply in notebook infrastructure and open source projects such as nteract. In this talk, Michelle Ufford and Kyle Kelley share interesting ways Netflix uses data and some of the big bets the company is making on notebooks. Topics will include architecture, kernels, UIs, and Netflix’s open source collaborations with projects such as Jupyter, nteract, pandas, and Spark.

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Predictive Analytics for IoT Network Capacity Planning: Spark Summit East tal...

The Internet of Things (IoT) is a growing network, supporting a wide variety of service types with specific network requirements that differ from traditional human type communications. This has led to emergence of dedicated IoT network standards. To optimize investments for dedicated network infrastructures, we’re investigating a dynamic approach in network capacity planning to accommodate multiple IoT traffic types over a cellular network, while maintaining their specific requirements. We studied models of IoT traffic and used machine learning in prediction and scheduling of future workload under heterogeneous and variable traffic conditions when human-type and machine-type communications are mixed. An integrated analytics framework including Hadoop and Spark were deployed for experimentation and a number of capacity planning use cases were implemented to verify the accuracy of the method.

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Using Apache Spark for Intelligent Services by Alexis Roos

Salesforce is developing Einstein which is an artificial intelligence (AI) capability built into the core of the Salesforce Platform. Einstein helps power the world’s smartest CRM to deliver advanced AI capabilities to sales, services, and marketing teams – helping them discover new insights, predict likely outcomes to power smarter decision making, recommend next steps, and automate workflows so users can focus on building meaningful relationships with every customer. Salesforce is using Apache Spark (batch, streaming, GraphX and ML) to power the Einstein platform and services. In this keynote and demo, Alexis will highlight how Salesforce is building intelligent Services for Einstein using activity data by leveraging Spark and Databricks to scale data science and engineering.

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Significant amount of effort is required to transform a machine learning (ML) model into a useful machine learning product. The incorporation of ML into real world applications almost feels like "1% algorithm and 99% perspiration". I will share with you my team experience in building 3 ML products at Zendesk. I will also discuss some real-world problems and scaling complexities you may encounter when building these products at web scale. Close collaboration with different groups including product, engineering and data science is imperative to strike the balance between model performance, scalability and computational efficiency. The talk mainly focuses on scaling our model building infrastructure with an aim to build at least 50,000 models a day. This is achieved as part of our efforts to deliver a ML product called Content Cues. In a nutshell, Content Cues summarizes text from customers support tickets to form insightful topics. It combines multiple ML algorithms including deep learning, clustering and other natural language processing approaches. These ML algorithms are then run through tens of thousands of eligible Zendesk customer data every day. My talk will cover the following topics: How we implement a horizontally scalable model building and model serving pipeline by combining AWS EMR, AWS Batch and Kubernetes How we tune the model building pipeline to optimize cost and efficiency without compromising resiliency Challenges in model monitoring, model versioning evolution and capturing of user feedback Speaker: Wai Chee Yau

How the IoT market may change our digital life thanks to the Data Tsunami it ...

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Laurent Marchou is the IoT & Robotics Director running the open-innovation product marketing team of Orange Innovation Department (Technocentre) thanks to products like Datavenue, Ub-y or LoRa starter kit. He loves working on disruption topics and was in charge of Broadband offers for Wanadoo (a long long time ago :) ) and Product Marketing Manager on mobile market. Laurent is graduate from ESME Engineering School (Telecom Dept with TelecomParisTech) and from HEC with an advance certificate (Business Unit Management)

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Saving Human Lives with the IoT

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Overview presentation of the CPaaS.io project given at the first year review meeting in Tokyo on October 5, 2017. Disclaimer: This document has been produced in the context of the CPaaS.io project which is jointly funded by the European Commission (grant agreement n° 723076) and NICT from Japan (management number 18302). All information provided in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and NICT have no liability in respect of this document, which is merely representing the view of the project consortium. This document is subject to change without notice.

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Snap4City November 2019 Course: Smart City IOT platform installation, deploy,...

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• Snap4City Architecture • Snap4City: Smart City IOT as a Service • Snap4City Living Lab For Collaborative Work • Smart City Development Life Cycle • Analysis and Design for Innovation (Co-Creation and Co-Working) • Development Tools • How to Add Functions that are not present in the Platform • Snap4City vs Fi-Ware • Snap4City vs State of the Art Solutions • Snap4City Services: Consulting and Developing • Snap4City vs Snap4Industry 4.0 • Installing Snap4City • The view of the Administrator • Monitoring Resource Consumption and Traffic • Managing and Monitoring Data Traffic in the BackOffice • Auditing Activities • Managing Back Office processes via Containers • Acknowledgement

Flink's Journey from Academia to the ASF

Fabian Hueske

Apache Flink is a project with a very active, supportive, and continuously growing community. Last year, Flink was among the top ten projects of the Apache Software Foundation with the most traffic on user and development mailing lists. Looking back, Flink started as a research prototype developed by three PhD students at TU Berlin in 2009. In 2014, the developers donated the code base to the ASF and joined the newly founded Apache Flink incubator project. Within three years, Flink grew into a healthy project and gained a lot of momentum. In my presentation, I will discuss Flink's journey from an academic research project to one of the most active projects of the Apache Software Foundation. I will talk about the academic roots of the project, how the original developers got introduced to the ASF, Flink's incubation phase, and how its community evolved after it graduated and became an ASF top-level project. My talk will focus on the decisions, efforts, and circumstances that helped to grow a vital and welcoming open source community.

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INTERFACE, by apidays - APIs: the next 10 years June 8, 9 & 10 2022 The Evolution of Data Movement Michel Tricot, Co-founder and CEO at Airbyte ------------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/ Deep dive into the API industry with our reports: https://www.apidays.global/industry-reports/ Subscribe to our global newsletter: https://apidays.typeform.com/to/i1MPEW

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Snap4City has been created in response to Select4Cities PCP (http://www.select4cities.eu/) call as an open, standardized, data-driven, service-oriented, user-centric platform enabling large-scale co-creation IOT/IOE applications and services for Helsinki, Copenhagen and Antwerp. Snap4City is 100% open source: robust, scalable, easy to use solution, provides tools for co-creation of mixt data driven, stream and batch processing, GDPR, and city dashboards. extending with IOT/IOE the semantic reasoner of Km4City https://www.km4city.org Km4City has been validated in multiple devices (PC, Android, Raspberry, ..), and domains: mobility and transport, tourism, health, welfare, social and cities such as Florence, Pisa, Arezzo, and large area of millions on inhabitants as Tuscany and million of data per day. The innovation is mainly related to semantic reasoning, IOT interoperability, microservices, automated dashboard production, .. thus setting up smart city solutions in a snap Serve as a City Dashboard, App User Interface, etc. Real time and historical data, any device, sensors and actuators Sensors, KPI, maps, data trends, real time data, charts, etc. Referral / historical data, and Open Data: shadow, access (API, storage, any protocol), production of OD, export Data Driven Real Time communication & processing: IOT Applications, IOT edge, multiple operating systems, embedded systems, MicroServices in/out data driven from/to the field into: applications, notifications, etc. Data Analytics: Machine Learning, statistics, reasoning, … Serve as Living Lab: open innovation, coworking; collaborative work; sharing: data, processes, dashboard, experiences, solutions, …. Experimented on large scale cases

The History of Software Architecture: In the eye of the practitioner

Patricia Lago

Software architecture (SA) is celebrating 25 years. This is so if we consider the seminal papers establishing SA as a distinct discipline, and scientific publications that have identified cornerstones of both research and practice, like architecture views, architecture description languages, and architecture evaluation. With the pervasive use of cloud provisioning, the dynamic integration of multi-party distributed services, and the steep increase in the digitalization of business and society, making sound design decisions encompasses an increasingly-large and complex problem space. The role of SA is essential as never before, so much so that no organization undertakes ‘serious’ projects without the support of suitable architecture practices. But, how did SA practice evolve in the past 25 years? And what are the challenges ahead? There have been various attempts to summarize the state of research and practice of SA. Still, we miss the practitioners’ view on the questions above. To fill this gap, we have first extracted the top-10 topics resulting from the analysis of 5,622 scientific papers. Then, we have used such topics to design an online survey filled out by 57 SA practitioners with 5 to 20+ years of experience.

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