Published Dec 3, 2018

Pachyderm's Kubernetes-based infrastructure for AI

Discover how Pachyderm leverages Kubernetes to streamline AI workflows in this insightful episode with Joe Doliner, exploring the integration challenges, data processing automation, and transformative tools like data versioning and provenance tracking essential for optimizing AI pipelines.

Episode Highlights

Topics covered

Episode Highlights

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Dexa/Practical AI

Pachyderm's Kubernetes-based infrastructure for AI

Topics covered

Popular Clips

Open Source Data Science

Data Storage Trade-Offs

JavaScript Panel Discussion

Data Science Infrastructure

Optimizing AI Pipelines

Data Science Infrastructure

Unified Data Processing

Liberating Data Science

Overcoming Infrastructure Challenges

Open Source Challenges

Automating Jupyter Notebooks

Streamlined Data Interface

Data Infrastructure Challenges

Episode Highlights

Infrastructure Challenges

Data Processing Automation

Pachyderm Introduction

Related Episodes

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End-to-end cloud compute for AI/ML

AI-powered scientific exploration and discovery

TensorFlow in the cloud

Self-hosting & scaling models

Serverless GPUs

The ins and outs of open source for AI

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Data science for intuitive user experiences

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Pachyderm's Kubernetes-based infrastructure for AI

Topics covered

Popular Clips

Episode Highlights

Infrastructure ChallengesJoe Doliner explores the critical role of Kubernetes in Pachyderm's infrastructure and the challenges of integrating it with existing systems. He highlights the flexibility Kubernetes offers and the trade-offs involved in data storage and system integration.

Infrastructure Challenges

Data Processing AutomationJoe Doliner discusses the transformation of Jupyter notebook workflows into automated pipelines using Pachyderm. He outlines the steps for optimizing AI pipelines and configuring Pachyderm for efficient data processing.

Data Processing Automation

Pachyderm Introduction

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