NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works

Implementing AI applications based on machine learning is a significant topic for organizations embracing digital transformation. By 2020, 30% of CIOs will include AI in their top five investment priorities according to Gartner’s Top 10 Strategic Technology Trends for 2018: Intelligent Apps and Analytics. But to deliver on the AI promise, organizations need to generate good quality data to train the algorithms. Failure to do so will result in the following scenario: "When you automate a mess, you get an automated mess."

This webinar covers:

- An introduction to machine learning use cases and challenges provided by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton and top data science and big data influencer.
- How to achieve good data quality based on harmonized semantic metadata presented by Andreas Blumauer, CEO and co-founder of Semantic Web Company and a pioneer in the application of semantic web standards for enterprise data integration.
- How to apply a combined approach when semantic knowledge models and machine learning build the basis of your cognitive computing. (See Attachment: The Knowledge Graph as the Default Data Model for Machine Learning)
- Why a combination of machine and human computation approaches is required, not only from an ethical but also from a technical perspective.

In this webinar, Metadata.io CEO Gil Allouche will talk about the different ways AI is being used by marketers. From analyzing data to orchestrating new marketing campaigns, AI is powering marketing activities in new and exciting ways and affecting interactions throughout the entire customer lifecycle. As an example of how AI can have a tremendous impact on marketing practices, Gil will focus on its role in lead generation. Webinar attendees will learn:

- What Machine Learning is in relation to AI and how it connects your data to find patterns
- Examples of how machine learning can identify target audiences, including the 20 percent that creates 80 percent of your revenue
- How AI technology can help marketers prioritize their budgets to focus on the most effective programs
- Starting with small, iterative uses of AI in marketing can be the most effective way to understand what will yield the most ROI

Gil Allouche founded Metadata.io to make demand generation easy for non-technical marketers. The Metadata.io platform and AI Operator evolved from Gil's experiences hacking various marketing and CRM systems to get the solutions he needed.

Emerging trends in AI and Machine Learning will impact nearly every aspect of the way organizations create and capture value in the years to come. A new set of opportunities and threats will emerge and change the landscape of many industries.

Join IRI and RTI as we interview Sam Adams, IBM Distinguished Engineer, IBM Research, to learn more about emerging trends in the fields of AI and Machine Learning and the ways organizations can prepare for an Intelligent Future.

Artificial Intelligence, Machine Learning, Virtual Reality, and intelligent voice assistants offer the potential to drastically improve how people work with data and collaboration systems. In this webinar we'll share research on how organizations are:
• Evaluating and adopting AI and related technologies
• Building business cases to justify investment
• Measuring and monitoring success

We'll look at specific examples of AI technologies in the context of communications and collaboration, and provide attendees with actionable guidance as they develop their own AI evaluation and implementation plans.

What are the top use cases in AI? Or should we say, what will be the top use cases in the next 5-10 years? How are companies using the disciplines of Machine Learning, Deep Learning and HPC? What is needed to enable faster adoption? What technology changes are expected or required? Evaluator Group and Tractica, leading analyst firms on the topic of Artificial Intelligence come together to discuss these topics of market adoption and technology requirements.

This is an INTERACTIVE session. We will start with short presentations then open up the Fireside chat. The audience is invited to submit their questions and participate in what will be a lively discussion.

AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Geordie Kaytes will share a framework for building customer-centered AI products. You’ll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.

Learn a framework for creating and communicating a vision that describes the overall direction of your AI product, a defined product strategy, a cross-functional roadmap aligned with the strategy, and a list of metrics that track progress towards the strategy

About the Speaker: Geordie Kaytes is the director of UX strategy for Boston-area UI/UX studio Fresh Tilled Soil and a partner at Heroic (https://www.heroicteam.com), a design leadership coaching firm that helps growing companies scale their digital product capabilities. A digital product design leader with deep experience in design process transformation and cross-functional expertise in design, strategy, and technology, Geordie has helped companies in a broad range of industries develop a 360-degree view of their product design processes. Previously, he did his obligatory tour of duty in management consulting. He holds a BA from Yale in political science. He is a coauthor of the Medium publication Radical Product.

As we move to the conversational UI and take advantage of NLP and AI in general, we change the way we interact with technology dramatically. The standard GUI is many times fully eliminated, leading to novel challenges in UX. Tasks are removed from the user’s oversight with invisible or seamless software, and the output is not always as expected. But sometimes that output is correct within the parameters given and simply perceived as an error.

Dennis will talk through where x.ai has encountered error perception issues as we seek to develop frictionless software, how we thought about the problem and the communication strategies we’re exploring to resolve it.

We are seeing a sea change in networking. SDN has enabled improvements in network telemetry and analytics.

In this presentation, I will talk about the current challenges that are out there and how the technology change is helping us to improve the overall network telemetry. Furthermore, I will share how deep learning techniques are being used in this field. Please join this webinar to understand how the field of network telemetry is changing.

This Webinar explains how Big Data, Artificial Intelligence, and Machine Learning is going to transform the future Banking Industry. Banks which can manage this Big Data evolution successfully will survive and thrive, and give a more holistic and personalized customer service, thereby increasing their revenues tremendously.

The key takeaway from this Webinar is that “Right information at the right place and the right time is going to be the real money and will shape the future of Banking Industry.”

Tariq is a Fintech Expert, writer, and thinker based in Toronto Canada and is currently working on an initiative to disrupt the conventional Banking Industry with “Big Data Predictive Analytics Model” of his startup.

In this webinar, Mustafa Kabul, Principal Data Scientist, SAS, will provide an introduction to deep learning and its applications.

Mustafa is a data scientist in the Artificial Intelligence and Machine Learning R&D at SAS, where he leads innovative projects for SAS’s next-generation AI-enabled analytics products, including applications of deep learning. His current focus is on applying deep reinforcement learning to operational problems in the CRM and IoT spaces. An operations research expert working at the interface of machine learning and optimization, previously, he developed distributed, large-scale integer optimization algorithms for marketing optimization problems. Ever the optimization enthusiast, Mustafa always looks into ways to improve the algorithms. Nowadays his favorites are the distributed stochastic gradient and online learning methods. Mustafa holds a PhD from the University of North Carolina at Chapel Hill, where his research focused on game theory models of supply chains selling to strategic customers.

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.