Digital Acceleration for the AI-Ready Enterprise

Machine learning and AI allow companies to better predict outcomes, automate processes, make decisions, and generate improved content. ML and AI are also a heavy lift for some companies as they require constant maintenance and support. These processes also require that your organization collect and store data in a manner that can be used by your data scientists and data engineers.

Attend this keynote session to learn the most common AI/ML pain points and how to overcome them. You will also learn:

• Which predictable and avoidable mistakes most often lead to the failure of ML/AI projects
• Why bias in data, data selection, and the algorithms used are crucial, and how to sure up your company’s data processes
• How to avoid feedback failures, model stability issues, and wonky metrics
• What’s next in AI/ML technology?

Abstract coming soon!

What does the ethically responsible delivery of AI look like? Many organizations struggle with the building of reliable and trustworthy AI, while also embracing long-lasting innovation.

In this timely and practical panel discussion, you will learn what AI data bias is and how to uncover it within your organization, as well as how to embed privacy and ethics in all AI designs/programs.

You will also learn:

• Ways to ensure that data is clean and unbiased before it is used in analytics or machine learning
• How to create a culture where ethical data is embraced across the organization
• Best practices for compliance with data privacy laws when handling and protecting sensitive information (liability and security)

Abstract coming soon!

Abstract coming soon!

AI and cloud computing when properly managed are powerhouse process and profitability enhancers! However, the combination of these technologies raises a myriad of cost, reliability, security and productivity issues.

Attend this keynote session to learn how to address the most common cloud-based AI/ML issues while lowering cloud spend.

You will also learn:

• How to make your AI-driven cloud computing efforts more strategic and insight-driven
• What often hinders the advantages of cloud-based machine learning algorithms
• Why prediction speeds are a primary concern, and what to do about it
• Best practices for intelligent data security

A recent industry study showed that 85% of Machine Learning (ML) projects fail to deliver value! That means big financial losses and oodles of wasted expert hours!

There are numerous (avoidable) mistakes that lead to the failure of ML projects. However, a lack of quality metrics is one of the most common.

Attend this panel to learn why great Machine Learning starts with high quality data, and which metrics will really make or break your ML efforts.

You will also learn:

• How top companies are measuring the success of Machine Learning products and processes
• Ways to strike a balance between measuring business performance and model performance
• Methods for defining and quantifying success
• Smart (and not so smart) model evaluation metrics