Building & Deploying Artificial Intelligence & Machine Learning Applications on Kubernetes

Dive into end-to-end AI/ML on Kubernetes: recap core k8s primitives, explore the Kubeflow control and data plane, and master installation via kfctl or Operator. Launch and manage Jupyter notebooks with PVC and S3/MinIO attachments, customize environments with GPU quotas, and build your own ML-ready container images. Orchestrate training, tuning and serving with Pipelines, Katib and KServe, all secured behind Dex/OIDC multi-user isolation. Hands-on labs guide you through CRDs, GitHub-backed authentication and real-world MLOps workflows.

About the course

Accelerate your AI/ML projects by learning how Kubernetes empowers reproducible, scalable model development and deployment. Start with a foundations recap—Pods, Services, ConfigMaps—and progress to Kubeflow components: Notebooks, Pipelines, Metadata, Katib and KServe. Install and verify Kubeflow, configure GitHub-based Dex authentication, and enforce per-user resource quotas. Through labs you’ll spin up JupyterLab servers, attach persistent storage, build custom images, and deploy production-grade inference services.

Course details

What's included

Course content

Foundations & Prerequisites

  • Kubernetes recap: Pods, Services, Deployments, ConfigMaps, Secrets
  • Machine Learning Overview
  • Artificial Intelligence Overview
  • Role of Kubernetes in AI & ML

Kubeflow Architecture & Installation

  • Kubeflow components overview (Notebooks, Pipelines, Metadata, Katib, KServe)
  • Lab: Explore Kubeflow CRDs in your cluster
  • Lab: Install Kubeflow via kfctl (or Operator) and verify core pods
  • Authentication (Dex/OIDC) & multi-user isolation
  • Lab: Configure Dex with a GitHub connector

Jupyter Notebooks & Workspace Management

  • Kubeflow “Notebook” CRD & lifecycle
  • Lab: Launch a Notebook server from the UI
  • Underlying k8s resources (PVCs, ServiceAccounts)
  • Lab: Attach a PVC; mount a MinIO/S3 bucket
  • Customizing user environments (images, GPU quotas)
  • Lab: Build and register a custom JupyterLab image with pre-installed ML libraries

Pipelines & Workflow Orchestration

  • KFP SDK & DSL concepts
  • Lab: Write a 3-step pipeline (preprocess → train → eval) in Python DSL
  • ContainerOp vs. pre-built components
  • Lab: Compile & upload the pipeline; run it and inspect logs
  • Pipeline parameters, artifacts, and caching
  • Lab: Enable experiment tracking and pipeline caching

Hyperparameter Tuning with Katib

  • Katib CRD model: Experiment, Trial, Suggestion
  • Lab: Define a KatibExperiment for your training container
  • Search algorithms (Grid, Random, Bayesian)
  • Lab: Run a Bayesian optimization; view trial results in the UI
  • Metrics collection & early-stopping
  • Lab: Extract the best hyperparameters and retrain your model

Model Serving (KServe)

  • InferenceService CRD: predictor, explainer, transformer
  • Lab: Package a trained model (SavedModel or joblib) into S3/MinIO
  • Autoscaling & GPU vs. CPU resource configs
  • Lab: Deploy an InferenceService and test with curl or Python
  • Canary & blue-green rollouts
  • Lab: Configure traffic splitting for A/B testing

Metadata, Tracking & Pipelines UI

  • ML Metadata (MLMD) fundamentals
  • Lab: Visualize pipeline runs and artifact lineage in the UI
  • Artifact lineage and reproducibility
  • Lab: Hook an MLflow server into your pipeline for metrics logging
  • Integrating MLflow or Weights & Biases
  • Lab: Query past runs via the Metadata API

Security, RBAC & Multi-Tenant Isolation

  • k8s RBAC essentials
  • Lab: Create separate namespaces for teams with granular RBAC
  • Namespaces, NetworkPolicies, and ResourceQuotas
  • Lab: Apply NetworkPolicies to isolate notebook traffic
  • Securing MinIO/S3 and Ingress TLS
  • Lab: Issue certs via cert-manager and secure the Ingress

Monitoring, Logging & Alerting

  • Prometheus metrics (Pod, GPU, pipeline durations)
  • Lab: Scrape Kubeflow and custom app metrics in Prometheus
  • Lab: Build a Grafana dashboard showing pipeline health
  • Centralized logs (EFK or Loki) and alert rules
  • Lab: Ship logs from notebooks and pipelines to Elasticsearch or Loki

Frequently asked questions

Is this training live or pre-recorded?
The training is live instructor led training which is available in classroom as well as online format. We also record every training session which is then uploaded to our student portal.
How will I join the live online training?
The live online training is conducted via the zoom software, we will be providing you with the zoom meeting link to join the training.
How many students are there in a single batch?
On an average one batch will have a maximum of 18 students. We keep smaller batch sizes to promote interaction between the students and the instructor.
How can I practice the labs?
We will provide you with online labs. If needed, we can also provide you with the software required to create your own labs.
Do you offer payment flexibility?
Yes, we provide zero interest EMI options.
Can I attend the training in classroom?
Yes, our classroom training location is in New Delhi near Lajpat Nagar metro staton.