Name: How to Get Your Machine Learning Project Past the Pilot Stage
Start: 2022-04-19T16:00:00Z
End: 2022-04-19T16:00:17.000Z
Location: BrightTALK
Rating: 0

This channel covers the advent of artificial intelligence in business and society. Join the discussion with webinars and videos covering everything from neural networks, to computer vision and NLP, to machine learning and AI application in the real world.

As organizations are looking for ways to stay on top of an evolving threatscape, many are
looking towards AI to enhance their security strategies and fend off cyber attacks before they
happen. In the 2022 CIO and Technology Executive Survey by Gartner, 66% of respondents
reported that they expected to increase cyber and information security investments for the
next year. More than half of respondents reported that they planned to heavily invest in
business intelligence and data analytics.

However some security leaders are cautious about adding artificial intelligence into their
security arsenal. Despite rapid advancements in AI, these solutions can also come with their
own unique set of drawbacks. Some skeptics say that AI technology is still immature and
that there may be simpler, more cost-effective solutions. With all the interest surrounding AI
in cybersecurity, it’s important for organisations to establish realistic expectations.

Join this panel to learn more about:
- The current state of AI in security
- What CISOs and their teams should keep in mind about AI
- How AI impacts your workforce strategy
- And more!

Speakers:
- Dan Lohrmann, Field CISO at Presidio
- Earl Duby, CISO at Lear Corporation
- Aidan Walden, Director, Public Cloud Architecture & Engineering at Fortinet
- Warsamé Ahmed, Co-Founder & CEO at BR[AI|YT.ai

Using AI in Cyber Security: Boon or Bust?

AI is not a one-size-fits-all technology; each AI project is customized to solve a specific business problem, with machine learning models. These models rely on data and algorithms to address a project’s needs. Learn the seven steps to building an effective machine learning model in this five-minute rapid session that gets you up to speed without disrupting your day.

Learn in Less Than 5 Minutes: 7 Steps to Build a Machine Learning Model

Credit unions that seek digital solutions to improve overall operating efficiency and member satisfaction will hear how a large credit union successfully implemented process automation to achieve significant business goals. We will discuss the lessons learned, as well as best and worst decisions made along the way.  Join Ashwin Krishnamurthi, a Process and Automation Expert at a large credit union and Karen Diamond, VP of Enterprise Accounts at pureIntegration for this fascinating discussion.

Digitally Evolving Credit Unions: A Business Case for Automation

Enterprise Analytics are currently often the purview of a narrow group of data scientists and trained analysts, with business users bottlenecked by those experts, or left to rely on static BI dashboards that are too slow for data exploration, but the economy increasingly demands that front-line business decision makers need real-time self-service analytics to make decisions faster and improve the customer experience. 

In this talk, Peter Kacandes, a Sr. Technical Product Marketing Lead at Imply.io, will discuss:

The origins of this problem in the architecture of the current analytics tech stack, and the emerging hot analytics technology stack, which opens up self-service analytics on real-time big data to hundreds or thousands of business people at a company.  

How a real-time data store that is 10-100X faster than data warehouses or data lake query engines, combined with an intuitive and high-speed UI, can empower untrained “citizen analysts” such as marketing, product, and operations managers, to make myriad daily decisions faster and data-driven. 

The learnings of well-known companies who have implemented hot analytics for use cases such as user behavior, network, application and service performance monitoring, and real-time fraud detection.

Putting Big Data Self-Service Analytics in the hands of Business People

Far too many companies struggle with data-related issues such as integrating online and offline data, a lack of collaboration between teams, low data accuracy, and poor data accessibility. You already know that creating a single source of truth for your customer data is the first step to good customer data management. Having your data all in one place provides a single data set that everyone in the company can use. This is the key to making your tools and teams work better together. A Customer Data Platform (CDP) can do all this and more. CDPs are designed to normalize large amounts of data across a wide variety of systems gathering data on the same individual in very different ways. 

Have you heard of a CDP before and the value it brings?

In this webinar, you’ll learn:

-What is a CDP?
-What are the advantages of a CDP over a data lake?
-How can a CDP enable better business intelligence reporting?
-How CDPs can be leveraged with use cases designed for marketing, product and operations teams?
-And so much more!

About the presenter: 
Aimee applies her vast analytics, project management, and leadership experience to help our client teams deliver the best analytics consulting solutions possible. Specifically, she works closely with clients to develop reports and analyze data, identify trends and relationships between different types of data, draw appropriate conclusions, and translate findings into marketing and sales strategies that drive revenue. 

Aimee has more than 10 years of experience with web analytics solutions, including Adobe Analytics and Google Analytics, applying data-driven methodologies to business, product, sales, and growth initiatives. In addition to her strong analytics consulting expertise, she is a strategic thinker with the ability to influence decision making and a strong leader who ensures on-time, on-budget project completion.

Customer Experience Focused Business Intelligence - Better with a CDP

Today’s enterprises are embracing a deep and drastic transformation and modernization to deliver their customers 100% business uptime while being nimble, simple and agile from an operational standpoint. While micro-service architectures, hyper-automation, and cloud are the de-facto agreed technologies driving this massive transformation, their impact to IT and Cloud operations cannot be neglected. 

A software glitch in a micro-service functional component propagates fast and impacts many business services at the same time. Datacenter-Cloud borderless infrastructure increases the surface of failures, and prolongs the time to detect, triage and remediate the problem.

IT and Cloud operations need to refresh their decade-old tools, systems and processes to overcome todays challenges. Alert fatigue, difficulty in identifying the root cause of the problem, little to no time to analyze, inefficient communication, poor collaboration & poor remediation processes are their daily pain-points.

In this talk, we will introduce AIOps and Self-healing Enterprise, a new technology paradigm designed to address todays remote working environments to improve business uptime and reduce operational pain points. The benefits of AI predictive intelligence, collaboration and automation in a turnkey, SaaS ready solution that automates the end-to-end IT/Cloud/Devops lifecycle – from early detection, root cause analysis to effective remediation. In doing so, AIOps drives higher business uptime, reduces resources required to manage, shorter the mean-time-to-resolution (MTTR) and drastic reduce the cost of ownership.

Presented by:

Muddu Sudhakar
CEO, Aisera
Twitter: @smuddu
Linkedin: https://www.linkedin.com/in/muddusudhakar/

and

MuckAI Girish
Chief Revenue Officer, Appnomic Systems
Twitter: @muckai 
Linkedin: https://www.linkedin.com/in/muckaigirish/

Autonomous Enterprise and the Rise of AIOps in Remote Working Environments

A fundamental need in various industries is to find underlying causal relations and use them to improve the business outlook of companies. All companies bar none would like to understand why their sales are declining, what leads customers to buy, how to improve employee motivation, where to allocate resources and what is the best competitive strategy. 

The knowledge of causal relations is essential in achieving these goals. However, in most analyses, scientists draw inferences from correlation. However, correlation is not causation, and such correlative inferences can often be disastrously incorrect. 

This talk covers the following:

- An insight into the data science of Causal Discovery
- How to generate causal hypotheses to determine root causes of operational situations
- How to conduct “natural experiments” to validate these hypotheses from a historical dataset
- Real-world case studies of the successful application of causal discovery in industry

About the speaker:
Vijay Nadkarni is Vice President Artificial Intelligence at Tech Mahindra, responsible for AI technology
and customer engagements across multiple verticals including automotive, manufacturing, aerospace,
healthcare, retail, semiconductor and others. Previously, Vijay was VP and global head of AI at Visteon,
responsible for AI across its product lines, notably autonomous driving and infotainment. A veteran of
Silicon Valley, Vijay has co-founded multiple startups in AI and cloud computing of which Veraz
Networks, a VoIP company, had a NASDAQ IPO, and another, Wellcore, was acquired.

The Use of Causal Discovery in Understanding the Root Causes of Business Trends

In honor of International Internal Audit Awareness Month (May 2020), come hear from Greg Jordan, Nationwide's Senior Vice President and Chief Audit Executive, Pat Shanahan, AVP Information Technology Audit Office, Internal Audit and Emigdio Zambada, Nationwide's IA Data Analytics Director, discuss how their department expanded the use of data & analytics by building a continuous auditing program in the cloud.  Partnering with Covail, they leveraged advanced analytics like AI and have provided education and resources to all of their auditors to enhance their data analytics skill set.

Continuous Audit - Achieve More Through Analytics

By most estimates, fewer than 20% of machine learning projects make it past the initial pilot stage. In order to succeed, data scientists must work closely with developers and IT operations teams to plan for future needs and model success to help justify costs. This webcast will explore the biggest challenges to implementing a machine learning project, where teams get tripped up and how to avoid these roadblocks. It will also cover advice for IT leaders about how to scale and support a machine learning project as it grows.

How to Get Your Machine Learning Project Past the Pilot Stage

Machine Learning

Artificial Intelligence

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How to Get Your Machine Learning Project Past the Pilot Stage

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