Part 1: Accelerate AI lifecycles - innovate, operationalize and govern

When:  Jan 19, 2021 from 9:00 AM to 10:15 AM (PT)

Online Event Series on Accelerating Digital Transformation and AI-Powered Innovation


AI and hybrid cloud are crucial technologies that help organizations navigate a different normal. Business leaders are adopting new ways of predicting and optimizing outcomes and scaling up AI-powered innovation. To identify and act on new insights and patterns across the evolving business landscape, they are rapidly tackling challenges in data, models, architectures, talent, and processes. How do we unify the tools and govern the AI lifecycle?  In what ways can we ensure trust and compliance across all data, AI and open source assets? In this online event series, IBM and IBM business partners will discuss AI-powered solutions, best practices and lessons learned to speed time to production, drive productivity and growth, and manage risks and compliance on a modern information architecture on IBM Cloud Pak for Data.  

Register now and explore your path forward for digitally re-inventing and future proofing your data and AI strategy.


January 19 - Part 1:  Accelerate AI lifecycles - innovate, operationalize and govern

In  part 1 of the online series, we will explore the best practices in AI lifecycles, human-machine workflows, and security and governance for successful AI implementation. Featuring Anaconda and Trust Insights, we will cover:

  • Modern AI lifecycle and governance - demystify what happens when new data science projects start
  • What does a human do and what does a machine do?  How do domain experts and data science work together?
  • Best practices in governance - understand interpretability, security and vulnerabilities from lifecycle
  • Expert roundtable on skill sets, cultures, hybrid cloud deployment and much more


Michael Grant, Vice President of Services, Anaconda

Chris Penn, Chief Data Scientist, Trust Insights 

Julianna Delua, Data Science and AI SME, IBM Data and AI


February 2 - Part 2:  Retire technical debt and open up data science for all 

This second session will explore the topic of replatforming your legacy tooling to be ready for modern data science and AI. To succeed in AI, in addition to implementing new capabilities, you need to retire technical debt and bring all contributors in a unified environment. Featuring Prolifics, IBM Business Partner, we will discuss:

  • Key considerations when you decide to replatform
  • Architecting for the era of AI - talent, technology, process and business
  • Diversity of tools for diverse talent  - visual data science, programmatic data science and other tooling
  • How to get started with Watson Studio Premium for IBM Cloud Pak for Data


Michael Gonzales, PhD, Chief Data Scientist, Prolifics

John Radi, Global Sales Leader, Prolifics

Julianna Delua, Data Science and AI SME, IBM Data and AI


February 16 - Part 3: Operate trusted AI with model governance

The third session will explore how business and technical teams partner better to operate trusted AI in an enterprise.  Featuring Appen, we will cover

  • Typical issues facing enterprises on AI trust
  • Model drift in the real world
  • Managing operations with better model monitoring
  • Considerations for improving model performance


Dr. MingKuang Liu, Head of Data Sciemce, Appen. 

Eric Martens, Worldwide Digital Technical Engagement Lead, IBM Data and AI

Julianna Delua, Data Science and AI SME, IBM Data and AI


March 2 - Part 4: Top five use cases with AI and decision optimization

The fourth session will focus on common AI use cases and deployment patterns using AI and decision optimization

  • How a typical AI project starts and what to watch out for
  • Your ideal first AI use case
  • Five common use case patterns using Watson Studio Premium
  • AI use case checklist - business case, goals, tools, organization, resources, etc.


Nerav Doshi, Worldwide Digital Technical Engagement Lead, IBM Data and AI

Julianna Delua, Data Science and AI SME, IBM Data and AI


March 16 - Part 5: AI engineering: Pragmatic ways to build your ModelOps practice 

The fifth session will cover the topic of ModelOps and AI engineering.  There is a spectrum of technical disciplines that AI and data science teams must consider in operationalizing AI, including ModelOps, DevOps and DataOps.  

  • What is AI engineering and why does it matters
  • Typical pitfalls that organizations face in operationalizing AI
  • ModelOps patterns and options
  • Advancements in tools, organizational approaches and processes


Darrell Reimer, Distinguished Engineer, IBM Research

Julianna Delua, Data Science and AI SME, IBM Data and AI

Event Image
Register Now


Laura Cruceru