The landscape of AI infrastructure

Being that this is “practical” AI, we decided that it would be good to take time to discuss various aspects of AI infrastructure. In this full-connected episode, we discuss our personal/local infrastructure along with trends in AI, including infra for training, serving, and data management.

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Featuring:

• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

Our locally installed stuff:

• Jupyter
• Docker
• Python
• Go
• Postman

Where we see AI workflows running:

• AWS
• GCP
• Azure
• Kubernetes and KubeFlow
• On-prem workstations:
  • NVIDIA
  • Lambda Labs
  • System76

Experimentation / model development:

• JupyterLab
• Google Colaboratory
• AWS SageMaker
• Data Science platforms:
  • Domino
  • DataBricks
  • DataRobot
  • H2O.ai

Pipelining and automation:

• Pachyderm
• Airflow
• Luigi
• Model optimization:
  • OpenVino
  • TensorRT
  • TensorFlow Lite

Serving:

• MXNet
• TensorFlow serving
• Seldon

Monitoring/visibility:

• TensorBoard
• Netron
• Knock knock
• Prometheus
• ElasticSearch

Something missing or broken? PRs welcome!

Creators and Guests

Host

Chris Benson

Host

Daniel Whitenack

What is Practical AI?

Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more).

The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!