Certified Professional
AI Operations
Master the art of MLOps. Build, deploy, monitor, and scale production-grade machine learning systems using industry-standard tools and practices.
Duration
6 Weeks
Mode
Hybrid
Format
Weekend Hybrid
Program Fee
Why This Program Exists
A model on a laptop is an experiment; a model in production is a product.
The Bottleneck
Data Science teams are producing models faster than engineering teams can deploy them. 87% of models never make it to production.
The Skill Gap
Companies need engineers who understand both machine learning lifecycles AND robust infrastructure (Kubernetes, CI/CD).
The Solution
This program bridges the gap. Move beyond notebooks to mastering CI/CD pipelines, Kubernetes, and real-time monitoring.
Who This Program Is For
Engineers ready to architect the backbone of modern AI systems.
Data Scientists
Who want to own the end-to-end lifecycle and deploy their own models.
DevOps Engineers
Who want to specialize in the high-demand field of Machine Learning Operations.
Software Engineers
Who want to transition into building AI platforms and infrastructure.
IT Professionals
Who want to upgrade their skills for the AI era.
What You'll Become
A Production-Grade MLOps Engineer. You'll stop treating models as black boxes and start treating them as software artifacts—versioned, tested, deployed, monitored, and scaled automatically.
What You'll Learn
A structured path from manual scripts to automated, scalable ML platforms
MLOps Foundations & Experiment Tracking
Technologies you'll learn
Docker
Kubernetes
OpenAI
GitHub Actions
Scikit-Learn
Prometheus
Terraform
AWSDockerKubernetesOpenAIGitHub ActionsScikit-LearnPrometheusTerraformAWSDockerKubernetesOpenAIGitHub ActionsScikit-LearnPrometheusTerraformAWSDockerKubernetesOpenAIGitHub ActionsScikit-LearnPrometheusTerraformAWS
Industry Recognized Certification
Validate your engineering prowess. Earn a certificate that proves you can architect and manage the complex infrastructure behind modern AI systems.
- Shareable on LinkedIn & Resumes
- Demonstrates Production Experience
Who you can become
MLOps is one of the highest-paid specializations in AI. Prepare for critical infrastructure roles.
MLOps Engineer
Bridges the gap between data science and operations. Responsible for building robust pipelines to train, deploy, and monitor ML models.
Core Responsibilities
MLOps Engineer
Bridges the gap between data science and operations. Responsible for building robust pipelines to train, deploy, and monitor ML models.
Core Responsibilities
Engineer Intelligent
Production Systems.
Go beyond tutorials. Architect and deploy 6 complex applications that solve actual business problems.
Predictive Maintenance Pipeline
A complete training pipeline using DVC for data versioning and MLflow for experiment tracking, predicting equipment failures.
Real-time Fraud Detection API
High-throughput fraud detection service using FastAPI and Docker, deployed with Blue/Green strategies.
Drift Monitoring Dashboard
Automated monitoring system that detects data drift and model degradation, triggering retraining alerts.