Managing and analyzing data to inform business decisions
Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource.
Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.
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.
Hundreds of companies, both old and new, are providing technology designed to improve customer engagement, ultimately raising customer experience scores. Everything from contact center to CRM to user sentiment to in-app contextual communications to many more areas are part of the customer engagement movement.
This first episode of our Digital Customer Experience series serves as a tutorial of technology categories and providers assisting in any customer interaction and analytics to evaluate success.
In order to accelerate innovation and learning, the data science team at Uber is looking to optimize Driver, Rider, Eater, Restaurant and Courier experience through reinforcement learning methods.
The team has implemented bandits methods of optimization which learn iteratively and rapidly from a continuous evaluation of related metric performance. Recently, we completed an AI-powered experiment using bandits techniques for content optimization to improve the customer engagement. The technique helped improve customer experience compared to any classic hypothesis testing methods.
In this session, we will explain various use cases at Uber that this technique has proven its value and how bandits have helped optimize and improve customer experience and engagement at Uber.
Today’s ultra low-power sensors and wireless modules not only allow batteries to last longer but also make completely self-sustaining IoT devices possible. This webinar will overview energy harvesting methods including photovoltaic, piezoelectric, thermoelectric, and RF with a focus on indoor ambient light collection.
Practical applications and how to integrate solar energy harvesters into electronics will also be discussed. An indoor solar-powered Bluetooth sensor will be analyzed as a use case example.
For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence technologies, which enable ‘smart machines’ to simulate intelligent behavior and make well-informed decisions with little or no human intervention.
Join this webcast to learn and discuss the best practices and trends coming for AI in IoT.
About the speaker:
Ahmed Banafa has extensive experience in research, operations and management, with focus on IoT, Blockchain and AI. He is a reviewer and a technical contributor for the publication of several technical books. He served as a faculty at well-known universities and colleges, including the University of California, Berkeley; California State University-East Bay; San Jose State University; and University of Massachusetts. He is the recipient of several awards, including Distinguished Tenured Staff Award of 2013, Instructor of the year for 2013, 2014, and Certificate of Honor from the City and County of San Francisco. He was named as number one tech voice to follow by LinkedIn in 2016, his researches featured in many reputable sites and magazines including Forbes, IEEE and MIT Technology Review, and Interviewed by ABC, CBS, NBC and Fox TV and Radio stations.
The need to make IoT devices smarter is a trend that will continue long into the future, enabling the "smart" in Cities, Supply Chains and every other area IoT is making an impact.
Making small decisions at the edge on the device ensures that the devices remain smart at all times. Building the feature store provides the ability for ongoing training and validation of the machine and deep learning models. Without the feature store, this process becomes arduous.
Join this interactive webinar to learn more about:
- What is a feature store and how is it different from Data Lakes
- Gain an understanding of repeatable feature generation and best practices
- What are the technology considerations to enable feature stores
About the Speaker:
Gary is the Managing Director for AI, Data and Analytics at 6point6. With over 17 years’ of experience, Gary leads a team of data scientists and data engineers in the agile development of AI and Machine Learning solutions. The focus of the team is bringing a collaborative approach to analytics, underpinned by machine learning and data engineering. He believes mainstream business adoption of AI solutions are the key to accelerating innovation enabling businesses to out compete, reduce cost and ensure compliance.
Artificial Intelligence is a part of our daily lives through the use of technologies like virtual assistants such as Cortana, smart homes, and automated customer service. We also have the power of the Internet of Things technology in organizations. How can we put them together for success in our organizations?
Businesses are running the Red Queen's race not just to win, but to survive in a world where Artificial Intelligence and IoT are becoming the present as well as the future of technology, and ideas are developing into reality at accelerated rates.
How can you help your company to evolve, adapt and succeed using IoT and Artificial Intelligence to stay at the forefront of the competition, and win the Red Queen's Race? What are the potential issues, complications, and benefits that the future of technology could bring to us and our organisations, right now?
In this session, Jen Stirrup will explain the quick wins to win the Red Queen's Race in AI and IoT to help you and your organization to win the Red Queen's race.
This webinar explains how Big Data in the context of IoT is going to impact Financial Services Organizations and what steps these incumbent organizations need to undertake for benefitting from this mega trend instead of getting drowned by it.
The key take-away from this webinar is that “As financial services companies embark on a journey to gain a better understanding of customers in order to provide effective and differentiated services, the amount of data is growing, IoT is multiplying this growth of data, and data structures are becoming more complex. Fintech Organizations need to develop technical capabilities to handle this Big Data and turn it to their advantage.”
About the speaker:
Tariq is a Fintech Expert, writer and thinker based in Toronto Canada and is currently working on an initiative to disrupt the conventional Insurance Industry with “Block chain and IOT applications to the insurance industry” for his startup.
Legacy development approaches and tools simply aren’t architected to handle the enormous flow of real-time, event-driven, data streams generated by IoT devices, existing systems, and people.
In this session, learn how the revolution in event-driven application platforms enable innovative companies to develop, deploy and run real-time enterprise applications with dramatically reduced time-to-market, significantly lowered development and maintenance costs, and maximized agility in the face of requirements for continuous innovation and digital transformation.
At most organizations, conference rooms are less than perfect--and that affects the productivity of any meeting. Inconsistency is the cornerstone of meeting room space, whether it's the power outlet locations, wired or wireless connectivity, brand of digital whiteboard, or video conferencing capabilities. That's why many companies are investing in "Conference Room of the Future" initiatives.
Nemertes recently conducted research among 600+ global organizations, and this webinar will focus on best practices.
It will address the following:
• What technologies are crucial to effective meetings?
• Who should run the meeting-room overhauls for the most success --IT or facilities?
• How do advanced technologies, such as artificial intelligence and Internet of Things, play a role in meeting rooms?
• What should IT leaders look for in management and monitoring tools?
We look forward to sharing this information with you and answering your questions!
With the General Data Protection Regulation (GDPR) becoming enforceable in the EU on May 25, 2018, many data scientists are worried about the impact that this regulation and similar initiatives in other countries that give consumers a "right to explanation" of decisions made by algorithms will have on the field of predictive and prescriptive analytics.
In this session, Beau will discuss the role of interpretable algorithms in data science as well as explore tools and methods for explaining high-performing algorithms.
Beau Walker has a Juris Doctorate (law degree) and BS and MS Degrees in Biology and Ecology and Evolution. Beau has worked in many domains including academia, pharma, healthcare, life sciences, insurance, legal, financial services, marketing, and IoT.