Enabling Remote Diagnostics for IoT Devices

This webinar is part of BrightTALK's Founders Spotlight series, featuring fearless entrepreneurs and inspiring founders.

In this episode, Eva Murray & Andy Kriebel, Founders of Makeover Monday, will share their story of how they started the social data project, Makeover Monday, the challenges and successes they encountered along the way and how they overcame them.

This will be an interactive Q&A session and an excellent opportunity for entrepreneurs or professionals to have their questions answered.

This talk tells the story of implementation and optimization of a sparse logistic regression algorithm in spark. I would like to share the lessons I learned and the steps I had to take to improve the speed of execution and convergence of my initial naive implementation. The message isn’t to convince the audience that logistic regression is great and my implementation is awesome, rather it will give details about how it works under the hood, and general tips for implementing an iterative parallel machine learning algorithm in spark.

The talk is structured as a sequence of “lessons learned” that are shown in form of code examples building on the initial naive implementation. The performance impact of each “lesson” on execution time and speed of convergence is measured on benchmark datasets.

You will see how to formulate logistic regression in a parallel setting, how to avoid data shuffles, when to use a custom partitioner, how to use the ‘aggregate’ and ‘treeAggregate’ functions, how momentum can accelerate the convergence of gradient descent, and much more. I will assume basic understanding of machine learning and some prior knowledge of spark. The code examples are written in scala, and the code will be made available for each step in the walkthrough.

Lorand is a data scientist working on risk management and fraud prevention for the payment processing system of Zalando, the leading fashion platform in Europe. Previously, Lorand has developed highly scalable low-latency machine learning algorithms for real-time bidding in online advertising.

You can do a lot with a Raspberry and ASF projects. From a tiny object
connected to the internet to a small server application. The presentation
will explain and demo the following:

- Raspberry as small server and captive portal using httpd/tomcat.
- Raspberry as a IoT Sensor collecting data and sending it to ActiveMQ.
- Raspberry as a Modbus supervisor controlling an Industruino
(Industrial Arduino) and connected to ActiveMQ.

The 10x growth of transaction volumes, 50x growth in data volumes and drive for real-time visibility and responsiveness over the last decade have pushed traditional technologies including databases beyond their limits. Your choices are either buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional analytical applications (HTAP).

Learn some of the current best practices in building HTAP applications, and the differences between two of the more common technologies companies use: Apache® Cassandra™ and Apache® Ignite™. This session will cover:

- The requirements for real-time, high volume HTAP applications
- Architectural best practices, including how in-memory computing fits in and has eliminated tradeoffs between consistency, speed and scale
- A detailed comparison of Apache Ignite and GridGain® for HTAP applications

About the speaker: Denis Magda is the Director of Product Management at GridGain Systems, and Vice President of the Apache Ignite PMC. He is an expert in distributed systems and platforms who actively contributes to Apache Ignite and helps companies and individuals deploy it for mission-critical applications. You can be sure to come across Denis at conferences, workshop and other events sharing his knowledge about use case, best practices, and implementation tips and tricks on how to build efficient applications with in-memory data grids, distributed databases and in-memory computing platforms including Apache Ignite and GridGain.

Before joining GridGain and becoming a part of Apache Ignite community, Denis worked for Oracle where he led the Java ME Embedded Porting Team -- helping bring Java to IoT.

Attend this session to learn how to easily share state in-memory across multiple Spark jobs, either within the same application or between different Spark applications using an implementation of the Spark RDD abstraction provided in Apache Ignite. During the talk, attendees will learn in detail how IgniteRDD – an implementation of native Spark RDD and DataFrame APIs – shares the state of the RDD across other Spark jobs, applications and workers. Examples will show how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames.

Akmal Chaudhri has over 25 years experience in IT and has previously held roles as a developer, consultant, product strategist and technical trainer. He has worked for several blue-chip companies such as Reuters and IBM, and also the Big Data startups Hortonworks (Hadoop) and DataStax (Cassandra NoSQL Database). He holds a BSc (1st Class Hons.) in Computing and Information Systems, MSc in Business Systems Analysis and Design and a PhD in Computer Science. He is a Member of the British Computer Society (MBCS) and a Chartered IT Professional (CITP).

When monitoring an increasing number of machines, the infrastructure and tools need to be rethinked. A new tool, ExDeMon, for detecting anomalies and raising actions, has been developed to perform well on this growing infrastructure. Considerations of the development and implementation will be shared.

Daniel has been working at CERN for more than 3 years as Big Data developer, he has been implementing different tools for monitoring the computing infrastructure in the organisation.

As data analytics becomes more embedded within organizations, as an enterprise business practice, the methods and principles of agile processes must also be employed.

Agile includes DataOps, which refers to the tight coupling of data science model-building and model deployment. Agile can also refer to the rapid integration of new data sets into your big data environment for "zero-day" discovery, insights, and actionable intelligence.

The Data Lake is an advantageous approach to implementing an agile data environment, primarily because of its focus on "schema-on-read", thereby skipping the laborious, time-consuming, and fragile process of database modeling, refactoring, and re-indexing every time a new data set is ingested.

Another huge advantage of the data lake approach is the ability to annotate data sets and data granules with intelligent, searchable, reusable, flexible, user-generated, semantic, and contextual metatags. This tag layer makes your data "smart" -- and that makes your agile big data environment smart also!

With new technologies such as Hive LLAP or Spark SQL, do you still need a data warehouse or can you just put everything in a data lake and report off of that? No! In the presentation, James will discuss why you still need a relational data warehouse and how to use a data lake and an RDBMS data warehouse to get the best of both worlds.

James will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. He'll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution, and he will put it all together by showing common big data architectures.

The hype surrounding AI has reached a fever pitch, but practical uses that are delivering concrete, practical business results are here, and it’s time to get real.

From taking over the routine requests for customer service agents, reducing costs, speeding up service, and boosting customer satisfaction, to reliably helping marketers deliver what a customer wants, when they want it and where, AI is popping up everywhere, and companies need to figure out where they stand, fast, to stay competitive.

Join this VB Live event, hosted by Forrester Senior Analyst Brandon Purcell and Kayak Chief Scientist Matthias Keller to learn more about how to identify the AI use cases that can transform your business, and how to get started, stat.

You’ll learn:
* What technologies fall under the AI umbrella
* How companies like Kayak use AI to understand customers and personalize experiences
* How to identify the right AI use case for your business
* Common challenges firms face when implementing AI
* Why AI’s time in the sun has finally come, and why it’s here to stay

Speakers:
* Brandon Purcell, Principal Analyst, Forrester
* Matthias Keller, Chief Scientist, Kayak
* Martine van der Lee, Director of Social Media, KLM
* Pradeep Elankumaran, CEO and Co-Founder of Farmstead
* Rachael Brownell, Moderator, VentureBeat

AI isn’t a nice-to-have any more, it’s a must-have. There’s a reason why corporate giants like Google, IBM, Yahoo, Intel, Apple, and Salesforce are competing to snatch up private AI companies. In the first quarter of 2017 alone, 37 AI companies were swallowed whole.

Companies aren’t just using these AI tools to enable existing marketing strategies; they’re taking those strategies to the next level, going beyond simple retention and delivering active engagement. AI technology offers companies unprecedented insight into customer behaviors, patterns, and beliefs, allowing you to seamlessly anticipate customer needs and serve up hyper-personalized, emotionally resonant campaigns where and when they’re most welcome.

2018 is the year to seize the AI advantage. To learn more about the technology you need, the opportunities it unlocks, and what it takes to get your ball in the game, don’t miss this VB Live event!

In this webinar, you'll learn:
* What’s new in AI for 2018 -- and what’s coming down the pike
* How businesses are using AI to drive results
* How to go beyond customer retention and power customer engagement

Speakers:
* Brian Gross, VP Digital Innovation, Aeromexico
* Dan Wulin, Director of Data Science, Wayfair
* Michael Healey, President, Yeoman Technology Group
* Rachael Brownell, Moderator, VentureBeat

Sponsored by Lytics

Watson is a computer system capable of answering questions posed in natural language. Watson was named after IBM's first CEO, Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! (where it beat its human competitors) and was then used in commercial applications, the first of which was helping with lung cancer treatment.

NetApp is now using IBM Watson in Elio, a virtual support assistant that responds to queries in natural language. Elio is built using Watson’s cognitive computing capabilities. These enable Elio to analyze unstructured data by using natural language processing to understand grammar and context, understand complex questions, and evaluate all possible meanings to determine what is being asked. Elio then reasons and identifies the best answers to questions with help from experts who monitor the quality of answers and continue to train Elio on more subjects.

Elio and Watson represent an innovative and novel use of large quantities of unstructured data to help solve problems, on average, four times faster than traditional methods. Join us at this webcast, where we’ll discuss:

•The challenges of utilizing large quantities of valuable yet unstructured data
•How Watson and Elio continuously learn as more data arrives, and navigates an ever growing volume of technical information
•How Watson understands customer language and provides understandable responses

Learn how these new and exciting technologies are changing the way we look at and interact with large volumes of traditionally hard-to-analyze data.

After the webcast, check-out the Q&A blog http://www.sniacloud.com/?p=296