Name: Managing Machine Learning Projects
Start: 2022-09-21T10:00:00Z
End: 2022-09-21T10:00:59.000Z
Location: BrightTALK
Rating: 5

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.

Web application environments are more complex than ever. Many span multiple clouds, often include containers, are connected via APIs, and delivered via CDNs. While exploits against known vulnerabilities remain common, advanced campaigns use a variety of tactics to compromise web applications. An abundance of tools claiming to address these challenges has left many security teams unclear as to where to focus their attention. On top of this, security teams are worried about how to address increased usage of genAI in development, and attacks that leverage AI. Join John Grady and Melinda Marks as they discuss recent research on the application protection market and modern application security to help security teams solve the challenges they face.

Trends in Application Security and Increasing AI Adoption

Generative AI (GenAI) could contribute $2.6 trillion to $4.4 trillion annually to the global economy according to consulting firm McKinsey, but achieving business goals requires using GenAI in a trustworthy and secure manner. As enterprise business units pour resources to drive innovation, security teams need to avoid compliance violations and data loss. Security for GenAI is a many-layered cake requiring security teams to consider everything from the (sensitive) data informing the model to prompt responses that might result in (sensitive) data exfiltration. Join Todd Thiemann as he discusses recent research on data resilience and data loss prevention in the context of GenAI.

Data Security & GenAI Infrastructure: Facilitating Innovation While Avoiding Compromises

The introduction of AI has created an imperative for the transformation of virtually every business system. This means every aspect of apps and infrastructure are in transition, leaving security pros to react to a massively changing attack surface. How will security teams respond? Where are security leaders focusing first? How will security strategies need to change to respond to this critical transformation? Join Dave Gruber, Principal Analyst, Enterprise Strategy Group as he shares a fresh industry perspective and data from his recent research on this topic.

AI-led Security Operations Strategies: Are You Keeping Up?

Cybersecurity has traditionally focused on identifying, analyzing, and mitigating vulnerabilities. However, as attackers evolve, including utilizing AI, the world of vulnerability management must undergo a massive transformation. This presentation will explore how AI-powered systems can reshape each facet of cyber risk and vulnerability management, improving both defensive capabilities and hacker tactics. This webinar will teach you the innovations driving this new approach, the emerging threats resulting from AI-enhanced attacks, and the future of risk management in an AI-driven world.

The Future of Security Risk - Security Vulnerabilities in the AI Era

As AI investments surge across large enterprises everywhere, CIOs are facing a new spin on familiar challenges. The rise of "FOMO AI" (Fear of Missing Out on AI) is driving a renewed focus on issues like business engagement, board influence, team development, and change management. 

Despite these challenges, the wave of technological advancement also presents new career opportunities for CIOs at both the executive management and board levels. CEOs are adding new technology officer positions to their executive committees, and boards are eager for technology representation. 

In this session, Martha Heller, CEO of tech executive search firm Heller, discusses the future of the CIO career and shares unique insights on how to leverage AI to achieve both business and career success.  

Topics for discussion include: 
- How CIOs are educating their boards about AI value
- New technology executive roles on the executive committee
- Evolving career paths for CIOs.
- Levering AI leadership needs for Board Director positions.
- And more.

The Impact of AI on the CIO Career

GenAI is the shiny new object that has executives and techies salivating. Companies have launched thousands of GenAI prototypes, of which only a small fraction have made it into production. The reason? Data deadweight. 

A GenAI model is only as good as its data. Garbage in, garbage out. But given how eloquent GenAI can string together words and/or images, many unsuspecting users may not recognize errors, distortions, and downright hallucinations. 

This session will outline the data risks of GenAI and provide guidelines for how to address them. It will address data bias, data freshness, data access, and data accuracy--among other things--and describe governance techniques, architectural patterns, and tooling that can bolster data inputs and minimize the risks of GenAI to business organizations.

Data Deadweight: How to Overcome Your GenAI Handicap

This session will explore practical applications of AI and machine learning in driving efficiency, improving decision-making, and creating competitive advantages. Attendees will gain insights into real-world case studies, learn how to overcome implementation challenges, and discover strategies to integrate AI into existing business processes effectively.

Key Takeaways:
1. How to identify business processes that can benefit most from AI and machine learning.
2. Strategies to successfully implement AI while addressing common challenges.
3. Lessons learned from real-world applications to accelerate adoption in your organization.
4. Best practices for integrating AI into existing workflows to maximize value.

Transforming Business Operations with AI and Machine Learning

Advanced analytics capabilities like deep learning and NLP have unprecedented potential for business impact -- but only with the right foundation. This session from industry thought leader Donald Farmer will demonstrate how structured governance and model management practices create a "safety net" that empowers data teams to push technical boundaries. 

Through real-world examples of predictive modeling and NLP implementations, learn how leading organizations use robust model lifecycle management, ethical AI frameworks, and systematic testing approaches to accelerate rather than inhibit innovation. 

We will cover some important practical techniques for establishing governance that enables rather than constrains, helping analytics teams confidently explore cutting-edge approaches while maintaining reliability and trust.

Breaking Free Through Boundaries: How Robust Governance of Advanced Analytics Drives Innovation

Data is more than numbers; it tells stories. This webinar explores innovative techniques to humanize data by identifying underlying narratives. Discover how to interpret datasets as storylines, extract characters from data, and organize them into a plot. Perfect for data scientists, analysts, and storytellers, this webinar bridges the gap between technical analysis and creative art, empowering you to craft compelling, data-driven narratives that resonate with diverse audiences and objectives.

Beyond Data Analysis: Extracting Characters and a Plot From Data

Organizations are confronted with evolving database requirements crucial for harnessing AI's full potential, including generative AI. And customers are recognizing the powerful potential of building using their own enterprise data. As AI technologies advance, traditional databases often fall short in addressing the demands associated with AI to handle scalability, flexibility, and real-time data processing that can help extract the most valuable data insights that drive the businesses forward. 

In this talk, Steve Catanzano will explore how AI has forced the reshaping of data management constructs like mission-critical databases, and the desire to apply new tools such as Vector and RAG to build a trusted foundation needed to create new solutions driven by data and AI.

Rethinking Database Requirements in the Age of AI

Generative AI promises to make customer service staff, including contact center agents, more productive, creative, and less likely to quit. Contact centers, as a particular arena of customer service, employ millions around the world but has a notoriously high turnover rate. It follows then that companies and other organizations face an ongoing challenge of recruiting, developing, and retaining customer service employees. Research shows an overwhelming majority of organizations believe generative AI tools have the potential to improve customer service operations, and most customer service workers remain positive, if not extremely positive, about the usage of AI. 

Join Brian McKenna to find out how organizations can raise the strategic level of their customer service and experience programs, how they need to intensify their focus on the critical area of customer data security, and how they can harness AI, generative and traditional, to better achieve these business goals.

The Impact of GenAI on the Evolution of Customer Service Technologies

Understanding and applying generative AI requires a deep technical knowledge base, and many organizations struggle to keep pace with the dynamic market. Which model is best for our use case? How can we incorporate our own data? How can we get started with responsible AI? Moreover, implementing and scaling GenAI solutions demands substantial computational resources and robust IT infrastructure. These factors coupled with the need to comply with data privacy regulations and AI ethics norms are creating a troubling scenario for organizations that want to leverage this powerful technology but need help getting started and eventually scaling its use within the business. 

Knowing many organizations rely on partners to bridge skill gaps and navigate the intricacies of GenAI development, join Mike Leone as he highlights his latest research study on the growing partner ecosystem and its crucial role in supporting GenAI, especially as organizations rapidly ramp up initiatives.

Navigating the Generative AI Partner and Alliance Landscape

Generative AI has taken the world by storm since OpenAI introduced ChatGPT in late 2022, and it is widely considered one of the most potentially transformative technologies in many years. However, GenAI technology and its ecosystem are evolving quickly, and many organizations are struggling to stay abreast of the constantly changing landscape. 

As this nascent market emerges, organizations continue to seek guidance to help steer their deployments, asking: What are the emerging best practices? Which use cases are prevailing? How mature is the market and how is this maturity defined? Is it best to build, buy, or partner? The ever-evolving nature of the answers to these questions creates a complex scenario for organizations that want to leverage GenAI technology. Join Mark Beccue as he discusses recent research on the State of the Generative AI market.

The State of the Generative AI Market: Widespread Transformation Continues

In the current landscape, businesses face numerous challenges, including the proliferation of digital environments, applications, services, and data. These factors contribute to increased complexity and operational costs. 

Traditional IT operations, characterized by manual processes and siloed systems, are now struggling to keep up with modern business demands. This has led to inefficiencies, slow response times, and inconsistent performance. This talk will explore the evolution of IT operations over the past few decades, highlighting the shift from simpler, less interconnected systems to the complex digital environments we see today. 

 Join KFC's Manager of Business Intelligence Farid Sheikhi as he delves into these challenges and discusses their impact on business performance and agility, emphasizing the need for modern approaches to IT operations.

Transforming Legacy IT Operations for Modern Business

Imagine a world where your customers are your biggest fans, and your employees rave about their IT Operations Department. It's not a dream, it's within your reach!

We think our customers want 100% availability of business-critical services, but they expect all their devices and services to work seamlessly.
We don’t give it to them because it takes all our resources to support infrastructure and services. This leaves them suffering with reactive support, waiting for devices to fail and then logging an incident to get support…waiting…waiting…waiting.

AI as a strategy removes the resource constraint. AI enables us to shift our focus by using automation to manage, prevent, and correct technical issues. From cyber attacks to daily maintenance, tools using AI enable even small operations departments to do it all, transforming the employee and customer experience.

This webinar will offer ideas, tips, and tricks for using AI to transform the support experience, including the tools available and the effort required to use them. The good news is that many of the tools are already in your enterprise but not used to their fullest capabilities.

"AI" Spells Operational Excellence

Artificial intelligence for IT operations (AIOps) is transforming the way businesses manage and optimize their technology environments. This presentation explores the critical role of explainable AI (XAI) in the strategic deployment of AIOps solutions. While AIOps leverages machine learning to automate and enhance IT operations, the complexity of these models often creates a barrier to understanding and trust for decision-makers. Explainable AI bridges this gap by providing transparency into how AIOps systems make predictions and decisions.

This session will demystify key AIOps processes, highlighting the importance of XAI for stakeholders who require insights into model outputs to ensure reliability, accountability, and ethical governance. Through real-world use cases, we will examine how XAI fosters informed decision-making, risk management, and better collaboration between technical teams and business leaders. Additionally, we will discuss the strategic advantages of adopting XAI, including increased trust, regulatory compliance, and improved operational outcomes.

Demystifying AIOps: The Role of Explainable AI in Strategic Implementation

In the rapidly evolving world of IT operations, staying ahead of potential issues is crucial for maintaining system performance and minimizing downtime. This session, "Optimizing IT Operations: AIOps Strategies for Proactive Issue Resolution," brings together industry experts Cass Cooper, Monika Mital, and Gareth Edward Jones to explore how artificial intelligence for IT Operations (AIOps) can transform your IT environment.

Key topics include:
- Implementing AIOps for real-time monitoring and issue detection.
- Leveraging machine learning to predict and prevent system failures.
- Enhancing incident response times with automated resolution techniques.
- Best practices for integrating AIOps into existing IT workflows.
- And more.

Join us to discover how AIOps can empower your IT team to move from a reactive to a proactive stance, enabling smoother operations, improved system reliability, and a more resilient IT infrastructure.

Optimizing IT Operations: AIOps Strategies for Proactive Issue Resolution

Data breaches and privacy concerns are an increasingly important issue for organizations of all sizes, public and private. As a result, leaders are finding they need to rethink how they manage and protect sensitive information. 

This presentation looks at new, transformative trends in automation for data management which, in addition to being more efficient, can support data privacy and security. Industry thought leader Donald Farmer will explore the limitations of manual data handling and how automation can mitigate risks associated with human error, poor performance, and inconsistencies. You will learn about cutting-edge automated solutions that enable real-time monitoring, access control, and regulatory compliance. 

By adopting an automated approach - even just starting out on this path - organizations can not only safeguard their data assets but also build trust with customers and stakeholders.

It’s an important topic and one which will resonate with every data manager concerned with security and privacy.

Automating Data Management to Secure Data and Build Trust

The most critical and FORGOTTEN element in advanced analytics: ML, AI and MO

AI and Machine Learning in Data Engineering

The Business Analysis Benefits -- and Limitation -- Of AI and Synthetic Data

The Power of AI Cybersecurity

Analysts vs. Machines: AI/ML for cloud-scale data

Making the Transition from Legacy Systems to Modern Analytics Platforms

Cyber Security Issues and Challenges in AI

The Economics of Data and Analytics

Graph Foundations for Advanced Analytics and Collaboration

How to overcome your biases and get better results from data exploration

Supply chain planning with AI

Delivering Business Value Through Scaled AI

AI in BI: How to Kickstart Your Adoption Journey with Accenture

AI in BI: How to Kickstart Your Adoption Journey

Security Issues and Challenges in Machine Learning

Machine Learning & Artificial Intelligence

Sometimes people manage machine learning projects as if they were software development projects. Sometimes they use approaches that have evolved and "just work for them." And sometimes they don't manage them at all! But the community "in the know" has come up with approaches and techniques that work to remove risk from ML projects and create high quality outcomes without burning out valuable teams in the process. 

I've written this all up in the book Managing Machine Learning Projects: From Design to Deployment (Manning, 2022). In this talk, I'll give an overview of the methodology and the motivations behind it. Attendees will learn:
- How to get a project started; 
- What the team needs to focus on up front;
- Where the risks come from; 
- How to handle the model development process; 
- Why evaluation is fundamental to success; 
- How and why to integrate ethics into the process;
- What needs to be considered when integrating ML into applications.

Managing Machine Learning Projects

Machine Learning

Artificial Intelligence

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Managing Machine Learning Projects

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