Name: Learn in Less Than 5 Minutes: 7 Steps to Build a Machine Learning Model
Start: 2022-04-15T14:00:00Z
End: 2022-04-15T14:00:03.000Z
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.

Artificial intelligence and Machine learning (AI/ML) is a hot topic in every business at the moment, and there is a growing dialog about what constitutes an Open Model, is it the weights? Is it the data?

Those are important questions, but equally important is ensuring that the tooling and frameworks to train, validate, fine-tune, and perform inference are open source. Storage systems are a crucial component of these workflows, how can open-source solutions address the needs for high capacity and high performance?

Data is key to any and all AI/ML workflows, without it there would be no data to use as an input for model training, re-evaluation and refinement of models, or even just securely storing models once training is complete, especially if they have taken weeks to produce!

Open source solutions like Ceph can provide almost limitless scaling capabilities, both for performance and capacity. In this webinar, learn how Ceph can be used as the backing store for AI/ML workloads.
We’ll cover:
• The demands of AI on storage systems
• How open source Ceph storage fits into the picture
• How to approach Ceph cluster scaling to meet AI’s needs
• How to get started with Ceph

Ceph Storage in a World of AI/ML Workloads

Join us for an insightful webinar on the transformative impact of AI on networking. This session will delve into the various use cases of AI, the nature of traffic for different workloads, and the network impact of these workloads. We will explore the multiple networking challenges posed by AI and how Ethernet is evolving to meet these demands. Special focus will be given to congestion issues during model training, the role of Ultra Ethernet Consortium (UEC), and the specific requirements related to training large language models (LLMs) and other use cases.

Learning Objectives:
- Understand the different types of Network Topologies typically used with AI workloads.
- Identify the nature of traffic for various AI workloads and their impact on networks.
- Learn about the challenges Ethernet faces with AI workloads and the solutions being implemented.
- Explore a specific use case to see how Ethernet addresses bandwidth and congestion issues.

Don’t miss this opportunity to stay ahead in the rapidly evolving field of AI and networking. Register now to secure your spot and gain valuable insights from industry experts!

Ethernet in the Age of AI: Adapting to New Networking Challenges

This presentation examines the critical role of storage solutions in optimizing AI workloads, with a primary focus on storage-intensive AI training workloads. We will highlight how AI models interact with storage systems during training, focusing on data loading and checkpointing mechanisms. We will explore how AI frameworks like PyTorch utilize different storage connectors to access various storage solutions. Finally, the presentation will delve into the use of file-based storage and object storage in the context of AI training: 
 
Attendees will:
- Gain a clear understanding of the critical role of storage in AI model training workloads
 - Understand how AI models interact with storage systems during training, focusing on data loading and checkpointing mechanisms
- Learn how AI frameworks like PyTorch use different storage connectors to access various storage solutions.
- Explore how file-based storage and object storage is used in AI training

The Critical Role of Storage in Optimizing AI Training Workloads

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?

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

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

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

Artificial Intelligence

Machine Learning

Machine Learning Models

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Big Data and Data Management

As an IT professional, many of the problems you face are multifaceted, complex and don’t lend themselves to simple solutions. The information technology community features useful and free information technology resources. Join to browse thousands of videos and webinars on ITIL best practices, IT security strategy and more presented by leading CTOs, CIOs and other technology experts.

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

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