Instructor-led training / klique-me

Klique ML Engineer Certification

Master the productionization and scaling of machine learning (ML) workflows with Klique in this hands-on, scenario-driven course.

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Instructor-led

klique-me

Duration: 3 Days
Format: Live Online
Access: Multiple Timezones

Overview

About this course.

This class prepares students for the Klique Engineer certification. Klique is an open-source MLOps platform that enables teams to seamlessly track, orchestrate, and scale machine learning workloads across Kubernetes, cloud, and hybrid environments. By the conclusion of this hands-on training, you will return to work with the skills to deploy, secure, and operate a full Klique environment — from experiment tracking to GPU-powered model serving.

Throughout the course, you will learn to use Helm, Kubernetes, and cloud-native tools to manage Klique at scale. You’ll configure external data stores, automate agent scaling, integrate with Hugging Face and vLLM, and practice troubleshooting real-world Klique incidents. The curriculum combines scenario-based labs with production-focused simulations.

Objectives

What you'll learn

Deploy, secure, and operate a full Klique environment on Kubernetes and cloud-native infrastructure.
Automate and orchestrate machine learning pipelines and agent scaling across hybrid environments.
Configure external data stores and enterprise storage solutions for Klique.
Integrate Klique with open-source tools and LLM serving engines such as Hugging Face and vLLM.
Troubleshoot production incidents, agent queues, and system performance issues in MLOps workflows.

Audience

Who it's for

Machine Learning Engineers (suggested)
MLOps Engineers (suggested)
Data Engineers and DevOps Professionals scaling ML workloads (suggested)

Prerequisites

Before you start

Experience with Python programming (suggested)
Basic familiarity with Linux, Kubernetes, and Helm concepts (suggested)
Fundamental understanding of Machine Learning and MLOps concepts (suggested)

Outline

Course outline.

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1. Core Klique Architecture & Platform Setup

Lecture: Klique Architecture Topology and MLOps Ecosystem Role
Lecture: Kubernetes Infrastructure Requirements and Ingress Design
Lecture + Lab: Deploying Klique using Helm and Custom Values
Lecture + Lab: Storage Options and External Persistence Configuration

2. Agent Orchestration, Scaling & Pipelines

Lecture: Klique Agent Mechanics and Task Queue Governance
Lecture + Lab: Configuring and Deploying Scalable Klique Agents
Lecture + Lab: Dynamic Pipeline Automation and Task Execution
Lecture + Lab: GPU-Powered Execution and Resource Management

3. Enterprise Integration, Model Serving & Incident Management

Lecture: Model Serving Integration with Hugging Face and vLLM
Lecture + Lab: Deploying and Monitoring End-to-End Model Serving Endpoints
Lecture + Lab: Production Troubleshooting and Agent Queue Debugging
Lecture + Lab: Capstone Project: End-to-End MLOps Pipeline & Infrastructure Operations

For teams

Training a whole team?

Request private training for Klique ML Engineer Certification and we'll follow up with a quote for your group.

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