Lately Kaarlo has mostly been working with product management, all things kubernetes, public & private clouds and writing some business logic with Node/ TS.

For the past 5 years Joel has helped numerous clients move the needle from zero to one through product design and campaigns.

Passionate and ever-hungry full-stack developer. Focuses on building web applications using modern technologies, and practices.

Joonas has strong background in people management and software development with experience of leading lean and agile projects.

A web developer using modern technologies to create elegant solutions for clients’ needs. Is equipped with excellent communication and team working skills.  

For the past 10 years Kenji's been working with critical systems. Lately he has been been working with Kotlin, React, Postgres and all things k8s.

Nicolas background is in electronics and signal processing with two M.Sc degrees. Lately Nicolas has been working on k-nearest algos and infrastructure.

Introduction

Argo in a nutshell

The Role of Integration Engines

How Argo Serves as an Integration Engine

Key Benefits of Using Argo for Integrations

Conclusion

How Argo tools can be used as an Integration Engine, and how we implement it here at Distrikt.

Argo as an Integration Engine

Argo-Events in a Nutshell

Key Components of Argo-Events

Setting up Argo-Events

Service Account

The event source

The sensor

Let's try it out!

What is it good for?

Introduction to Argo Events, the Kubernetes Native Event-Driven Workflow Automation Tool

Event-Driven Workflow Automation with Argo Events

Argo Events: Event-Driven Workflow Automation

When designing new features from the scratch and presenting our work to the team, we easily overlook many edge cases causing a lot of un-necessary back and forth communication.

Designing New Features: A checklist for successful collaboration

Designing new features: a checklist for successful collaboration

Intro

Why you want to train and tune your Machine Learning models regularly?

A little recap

A close to real life example of tuning and training

The WorkflowTemplate manifest

CronWorkflow

Multi use

How to decide which hyperparameter set to use when training a model?

Training Machine Learning Models with Argo Workflows

Tuning and training machine learning models with Argo Workflows

What is Argo Workflows?

Why do we want to use it?

What does a model training pipeline look like?

Few things to take into consideration when designing training pipelines with Argo Workflows

Setup for local experimentation

Templating steps for reusability

Get training data

Set hyperparameters

Evaluation results

Add resources to k8s

The training pipeline (read: Workflow)

Continuous experimentation

At the early stages of a project using pre-trained machine learning models is a quick way to get started. While this practice can help you figure out the problem to be solved, you often discover that your problem is beyond the basics and therefore requires more attention. In this blog post, we demonstrate how we build a machine learning training pipeline running on Kubernetes.

Getting started with Training Models using Argo Workflows

Workflow Controller ConfigMap

Workflow

Argo Workflows - Pod Security

Preface

What is an OpenAPI Specification (OAS)?

The Design First and Code First approaches to developing API's

How we do API development in one of our internal projects

Why is API documentation so important 🧐

Cons of providing an OAS

Pros of providing an OAS

Recap

If you need help with working with APIs

This blog post discusses an alternative way of developing APIs in an internal project using a hybrid method between the Design First and the Code First approaches. It also describes why to provide an OpenAPI Specification, how it impacts the developer experience, and helps you understand some of the common pitfalls that you might stumble upon when you provide an OpenAPI Spec.

Working with APIs: Using OpenAPI Specification

Developing API's and the importance of providing an OpenAPI Specification

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