Hauke Brammer

MLOps on Kubernetes: Exploring Argo Workflows

Stop writing brittle glue code for your MLOps pipelines. See how Argo Workflows provides a Kubernetes-native way to define and execute complex processes declaratively.

MLOps on Kubernetes: Exploring Argo Workflows
#1about 3 minutes

Understanding the core principles and lifecycle of MLOps

MLOps applies DevOps principles to machine learning to automate and streamline the entire model lifecycle from data collection to deployment and monitoring.

#2about 4 minutes

Why Argo Workflows is a powerful Kubernetes-native engine

Argo Workflows is a Kubernetes-native engine that orchestrates complex, multi-stage processes as custom resources, eliminating the need for extensive glue code.

#3about 4 minutes

Building and running a basic workflow with YAML

A simple workflow is defined in YAML using templates for each step, which are then executed inside containers on a Kubernetes cluster.

#4about 4 minutes

Managing data files in pipelines using artifacts

Argo artifacts simplify data handling by automatically downloading input files from cloud storage into a container and uploading outputs upon completion.

#5about 4 minutes

Orchestrating complex training jobs with DAGs

Directed acyclic graph (DAG) templates in Argo allow you to define complex workflows with multiple dependencies, enabling parallel and sequential task execution for model training.

#6about 4 minutes

Building resilient batch inference pipelines with retries

For reliable batch inference, Argo's retry strategies with configurable limits and backoff policies can automatically recover from transient failures in individual steps.

#7about 3 minutes

Evaluating if Argo Workflows is right for your team

Argo is ideal for teams already using Kubernetes to manage complex, multi-stage ML pipelines, but may be overkill for small projects or teams without Kubernetes expertise.

#8about 1 minute

Integrating Argo with tools like Argo CD and MLflow

Argo Workflows can be used alongside Argo CD for deployment and MLflow for experiment tracking, with Argo providing more flexible, language-agnostic container orchestration.

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Matching moments

Demonstrating the automated LLMOps pipeline in action
02:31 MIN

Demonstrating the automated LLMOps pipeline in action

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices

Automating complex LLM workflows with Argo Workflows
04:01 MIN

Automating complex LLM workflows with Argo Workflows

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices

Implementing a GitOps approach for end-to-end LLMOps
03:20 MIN

Implementing a GitOps approach for end-to-end LLMOps

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices

Orchestrating MLOps workflows for reliability
02:24 MIN

Orchestrating MLOps workflows for reliability

The state of MLOps - machine learning in production at enterprise scale

Q&A: MLOps tools for building CI/CD pipelines
02:49 MIN

Q&A: MLOps tools for building CI/CD pipelines

Data Science in Retail

Introduction to GitOps and the talk agenda
03:33 MIN

Introduction to GitOps and the talk agenda

Get ready for operations by pull requests

What MLOps is and the engineering challenges it solves
04:56 MIN

What MLOps is and the engineering challenges it solves

MLOps - What’s the deal behind it?

Understanding the role and challenges of MLOps
03:08 MIN

Understanding the role and challenges of MLOps

The Road to MLOps: How Verivox Transitioned to AWS

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