Episode 5: Artificial Intelligence in the Hybrid Cloud
In this episode, KJ Burke and Michael Traves from CDW and Allen Clingerman from Dell Technologies discuss what technology an organization needs to support AI programs, how to leverage reference architectures and data science pipelines, where to run various types of workloads, and the cost vs complexity of running AI on-prem and in the cloud.
Featured in this Episode
KJ Burke is an innovative and driven IT infrastructure architect with solid interpersonal and communication skills. He is currently the Principal Technology Strategist at CDW Canada, with over 20 years in the IT industry and plenty of experience in planning and deploying technology to improve business processes and drive measurable value.
Michael Traves has 25 years of experience in IT, and a wealth of knowledge founded in data management, high availability and disaster recovery design practices. As a Principal Solutions Architect at CDW, he advises on solution strategy and technology in DevOps, AI and the cloud, helping clients realize the benefits of modern application architecture with cloud-native design principles.
Allen Clingerman drives Dell’s Data Centric Workload and Solutions practice, helping partners build unique solutions underpinned by Dell Technologies to deliver higher partner margins, differentiation in the market and Dell Field Sales alignment. Focus areas include: SAP, Oracle, SQL, HPC, analytics, data lakes, AI/ML, hybrid cloud, containers and VDI.
In this episode, we discuss…
- Four technologies that you need to support AI workloads
- Whether it makes sense to move AI data into the cloud or to run certain workloads on premises
- Why processes need to change to support AI programs and what IT teams can do from an infrastructure standpoint to support their data science team
- The three different levels of maturity for organizations that are adopting AI, and how Dell Technologies can help
- Functional use cases for AI across healthcare, retail and customer service
- How to use real-time data collected at the edge and sensor data to support AI programs
- Structured and unstructured data sets and how to gather insights around them
- How pervasive do you want to make artificial intelligence in your organization, and where is that data going to live?
- The importance of public cloud usage models, and keeping track of costs over time
- The state of AI now and in the future
Findings from our Cloud Report
52% of CXOs stated their organizations are investing in cloud-based infrastructure and applications, as well as AI and ML technologies.
