Companies want AI, but the real challenge is deploying AI models in production, using GPUs efficiently, integrating ML with Kubernetes, and scaling AI workloads confidently.
Webinar - 13th June 2026
Time - 10 AM – 12 PM IST
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Most people talk about AI models. Enterprises need professionals who can deploy, manage, and scale AI workloads in real production environments.
Understand how AI models move from notebook experiments to production-ready APIs.
Learn why GPU resources matter and how AI workloads use compute infrastructure.
See how Kubernetes helps package, orchestrate, and manage AI applications.
Learn what enterprises need for reliable, scalable, and automated AI platforms.
Simple foundation first, then OpenShift AI concepts, then a practical live deployment demo.
A compact practical session designed to give clarity on AI infrastructure and OpenShift AI deployment.
AI vs ML vs LLM in simple language, why Kubernetes matters for AI, and why enterprises struggle with production AI.
Understand what OpenShift AI is and why it is important for enterprise AI and MLOps workflows.
Data Science Projects, Notebook environments, Jupyter integration, model training, model serving, and pipelines.
Watch a simple but sweet AI model deployment where the model is served as an API on OpenShift AI.
Learn how DevOps, Kubernetes, Linux, and AI skills combine for future-ready career opportunities.
These are the exact building blocks used in real AI platforms.
How teams organize AI work, experiments, notebooks, and resources inside OpenShift AI.
How Jupyter notebooks help data scientists develop and test models inside a managed platform.
How AI/ML models are trained and prepared before production deployment.
How a trained model becomes an API that applications can call in production.
How automation creates repeatable MLOps workflows for training and deployment.
How Kubernetes provides container orchestration for AI workloads.
Why GPU acceleration is important for AI workloads and how enterprises plan compute resources.
How production AI workloads are monitored, scaled, and made reliable.
This is not only theory. In the webinar, we will practically demonstrate how a simple AI model can be deployed and served using OpenShift AI concepts.
$ create data-science-project
$ launch jupyter-notebook
$ train simple-ai-model
$ deploy model-serving-api
$ test prediction endpoint
✅ Model deployed successfully
✅ API ready for production use
✅ Scalable with OpenShift AI
Highlight trainer credibility with clear photos, role, experience, and practical expertise.
Expert in Linux, Kubernetes, OpenShift, DevOps automation and enterprise infrastructure training. Add trainer experience, certifications and corporate training highlights here.
Perfect for learners and professionals who want to move from traditional DevOps skills to AI infrastructure skills.
Learn how enterprises build, deploy and scale AI applications using Kubernetes and OpenShift AI.
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