MLOps Bootcamp: Mastering AI Operations for Success – AIOps

MLOps Bootcamp: Mastering AI Operations for Success – AIOps-Complete Guide

MLOps Bootcamp: Mastering AI Operations for Success – AIOps is a comprehensive, end-to-end training program designed to help learners build, deploy, monitor, and maintain machine learning systems in production. The course goes beyond basic MLOps concepts by blending AI Operations (AIOps), DevOps practices, and modern ML tooling into one unified learning path.

Rather than focusing only on model training, this bootcamp emphasizes operational excellence, teaching how to manage ML systems reliably at scale—from development to deployment, monitoring, and long-term maintenance.

Course Snapshot

• Rating: 4.5 / 5

• Students Enrolled: 13,535+

• Skill Level: Beginner → Advanced

• Language: English (Auto captions), Spanish (Auto captions)

• Last Updated: January 2026

• Content Length: ~36.5 hours

• Format: On-demand video + articles

• Access: Full lifetime access

• Refund Policy: 30-day money-back guarantee

What This Course Focuses On

This bootcamp is designed to build strong operational foundations for AI systems, covering the complete MLOps lifecycle:

• Developing ML-ready Python workflows

• Versioning code, data, and experiments

• Packaging and deploying ML models

• Automating CI/CD pipelines

• Monitoring model performance and data drift

• Applying AIOps principles for observability and reliability

The course strongly emphasizes real-world production scenarios, making it suitable for learners aiming to work on enterprise ML platforms.

MLOps Zero to Hero

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Tools & Technologies Covered

Learners gain hands-on experience with a full production-grade MLOps stack, including:

• Core Development & Collaboration

  • Python

  • Git & GitHub

  • Linux

  • YAML

• Experiment Tracking & Model Management

  • MLflow

• Containerization & Deployment

  • Docker

  • Docker Compose

  • FastAPI

  • Flask

  • Streamlit

• CI/CD & Automation

  • Jenkins

• Monitoring & Observability

  • Prometheus

  • Grafana

  • WhyLogs

These tools are taught in context, showing how they fit together in real MLOps pipelines.

Skills & Concepts You’ll Learn

Python for MLOps

• Python fundamentals tailored for MLOps

• Data manipulation and ML workflow optimization

• Using Python across the ML lifecycle

Version Control & Reproducibility

• Git-based collaboration workflows

• Managing changes in ML projects

• Ensuring reproducible model builds

Model Packaging & Deployment

• Packaging ML models for production

• Containerizing ML applications with Docker

• Building scalable APIs using FastAPI

• Creating interactive ML apps with Streamlit and Flask

CI/CD for Machine Learning

• Designing CI/CD pipelines for ML workflows

• Automating training, testing, and deployment

• Using Jenkins for pipeline orchestration

Monitoring, Observability & AIOps

• Monitoring ML systems with Prometheus and Grafana

• Detecting data drift and anomalies

• Logging and observability with WhyLogs

• Debugging and maintaining production ML systems

Production ML Maintenance

• Updating and retraining models safely

• Managing long-term ML system performance

• Applying best practices for sustainable AI operations

Who This Course Is Best Suited For

• Aspiring MLOps Engineers

• Data Scientists transitioning to production ML

• ML Engineers and AI Engineers

• DevOps engineers working with ML systems

• Professionals interested in AIOps and AI platform engineering

Prerequisites

• Basic familiarity with Python is helpful but not mandatory

• Interest in machine learning, automation, or AI systems

• No prior MLOps experience required

Practical Value

This bootcamp stands out due to its:

• Broad yet practical coverage of MLOps and AIOps

• Strong focus on monitoring, observability, and reliability

• Emphasis on production readiness, not just experimentation

• Coverage of DevOps + ML + AIOps in a single learning path

Learners gain a systems-level understanding of how ML operates in real production environments.

Career Relevance

The skills taught align well with roles such as:

• MLOps Engineer

• AI Operations Engineer

• Machine Learning Engineer

• DevOps Engineer (ML-focused)

• AI Platform Engineer

The tooling and workflows reflect industry-standard MLOps practices.