AI & ML with DevOps Training Program

The Future is AI, and It Starts Here.
“Artificial Intelligence is no longer just a buzzword; it is the engine driving the global economy. At VervenTech, we believe that talent in Kashmir deserves world-class opportunities. Our comprehensive AI course with 24×7 lab access is designed to take you from a complete beginner to an industry-ready AI Engineer, right here in Srinagar.”

Set yourself up for success

Hands-on-Projects

Build 10+ Industry level Capstone projects to align with the industry skillset.

Tool Mastery

Learn Python, AI&ML Models, LLM, RAG, Devops, AWS, Linux, Git, IaC and much more.

Experienced Instructors

Learn from the best seasoned IT professionals and industry experts.

24×7 lab Access

Our trainees get access to dedicated labs 24×7 to complete the projects and labs.

100+ hands-on Labs

Our training is 90% Hands-on labs and 10% Theory.

Placement Support

We strive for a 100% placement record.

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Get the detailed curriculum covering AI, ML, DevOps, and Backend Engineering modules.

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Our AI & ML Engineering Program

Phase 1: Pre-requisites & Core IT & AI/ML Fundamentals

  • IT Infrastructure: CompTIA A+ & Networking Essentials
  • System Mastery: Linux Administration & Bash Scripting Automation
  • Virtualization: Understanding VMs & Container Concepts
  • Programming & DB Base: Python Basics & Flask Framework (Web Apps), PSQL
  • Mathematics for AI: Applied Statistics, Probability & Linear Algebra.
  • Data Science Stack: Master NumPy, Pandas & Matplotlib for data manipulation..
  • Core ML Algorithms: Regression, Classification & Clustering using Scikit-Learn.

Phase 2: Advanced AI, GenAI & Backend Engineering

  • Deep Learning: Master Neural Networks using TensorFlow & PyTorch.
  • Computer Vision & NLP: Build object detection systems and process human language (OpenCV/Spacy).
  • Generative AI & LLMs: Fine-tune Large Language Models (ChatGPT tech) and build RAG pipelines using LangChain.
  • AI Capstone: Build and train a production-grade AI model (e.g., Legal Doc Analyzer).
  • Python Fast-API backend
  • Building 3-Tier web-scale cloud-native Applications

Phase 3: MLOps, Cloud & DevOps Engineering

  • Containerization: Package AI models using Docker & orchestrate with Kubernetes (K8s).
  • AWS Cloud Native: Deploy on AWS (EC2, S3, Lambda, SageMaker).
  • Infrastructure as Code (IaC): Automate cloud setup using Terraform.
  • Config Management: Automate server provisioning with Ansible.
  • CI/CD Pipelines: Automate testing and deployment using Jenkins or GitHub Actions.
  • Cloud & Observability: Deploying on AWS (EC2, S3, Lambda) and setting up monitoring dashboards with Prometheus & Grafana.