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AI

Kubeflow

Hands-on AI and data analytics workshops — built around your team's real cases.

Duration
16h · 2 days
Who it's for

Ideal for teams that…

1 Developers and data engineers who want to enhance their skills in managing the ML lifecycle on Kubernetes
2 IT specialists looking to use Kubeflow to automate data processing and prediction in their organizations
3 Data scientists and analysts aiming to train and deploy ML models in a scalable production environment
Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

How to configure and manage Kubeflow on Kubernetes

How to deploy ML models using Kubeflow Serving and monitor their performance

How to perform exploratory data analysis (EDA) and train ML models with Kubeflow Pipelines

How to integrate Kubeflow with other ML tools and cloud platforms, and automate ML processes using CI/CD tools

Requirements

What you should know before we start

  • Basic knowledge of Python programming
  • Basic skills in working with Kubernetes
  • Basic understanding of machine learning
Program · 2 modules

What we actually do

Day 1: Introduction to Kubeflow and Platform Basics

M01
Day 1: Introduction to Kubeflow and Platform Basics
  • · Kubeflow fundamentals Introduction to Kubeflow and its architecture Installing Kubeflow on Kubernetes Data management and exploratory data analysis (EDA) Importing and processing data in Kubeflow Performing EDA with Kubeflow Pipelines
  • · Training models in Kubeflow Introduction to training components in Kubeflow Automating model training with Kubeflow Pipelines Training the first model Hands-on exercises: training a model on a real dataset Analysis and evaluation of model results

Day 2: Advanced Techniques and Practical Applications

M02
Day 2: Advanced Techniques and Practical Applications
  • · Advanced techniques for training models Using custom scripts for training models Leveraging GPUs and compute clusters to accelerate training
  • · Deploying and monitoring models Deploying models with Kubeflow Serving Monitoring and managing deployed models Model deployment and optimization Hands-on exercises: deploying a Kubeflow model Model optimization and hyperparameter tuning
  • · Integration with other tools and services (optional) Integrating Kubeflow with other ML tools and cloud platforms Using CI/CD tools to automate ML processes
Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.
How we work

From brief to retro in 30 days.

01

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

02

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

03

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

04

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

Quote within 48h of the brief
First session within 30 days
Pilot before the full decision
VAT invoice, payment in instalments possible

How we handle your data

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Optional marketing consents

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