MLOps Zero to Hero

MLOps Zero to Hero -Complete Guide

MLOps Zero to Hero is a practical, production-focused course designed to take learners from basic machine learning workflows to enterprise-grade MLOps systems. The course focuses on deploying, monitoring, scaling, and maintaining ML models in real-world environments, bridging the gap between data science experimentation and reliable production systems.

Rather than stopping at model training, this course emphasizes end-to-end ML lifecycle management, covering tools and platforms used by modern ML teams across startups and large enterprises.

Course Snapshot

• Rating: 4.7 / 5

• Students Enrolled: 10,014+

• Skill Level: Intermediate → Advanced

• Language: English (Auto captions)

• Last Updated: January 2026

• Content Length: ~12.5 hours

• Format: On-demand video + articles + resource

• Access: Full lifetime access

• Refund Policy: 30-day money-back guarantee

What This Course Focuses On

This course is built around production-grade MLOps, not just theory. It teaches how to:

• Track experiments and datasets

• Version models and data

• Deploy ML models reliably

• Automate ML pipelines

• Monitor models in production

• Scale ML systems using cloud-native infrastructure

The emphasis is on real tooling and real workflows, closely aligned with how MLOps is practiced in industry.

Tools & Technologies Covered

The course provides hands-on exposure to a modern MLOps stack, including:

• Experiment Tracking & Versioning

  • MLflow

  • DVC (Data Version Control)

• Containerization & Orchestration

  • Docker

  • Kubernetes

• Model Serving & Inference

  • KServe

  • SageMaker endpoints

• Pipeline & Platform Tools

  • Kubeflow

  • AWS cloud services

These tools are introduced in context, showing why and when each is used in a production ML system.

MLOps Bootcamp: Mastering AI Operations for Success – AIOps

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Skills & Concepts You’ll Learn

Core MLOps Fundamentals

• Understanding the ML lifecycle from training to deployment

• Common challenges in production ML systems

• Differences between research ML and production ML

Experiment Tracking & Reproducibility

• Tracking experiments with MLflow

• Versioning datasets and models using DVC

• Ensuring reproducible ML pipelines

Deployment & Serving

• Containerizing ML models with Docker

• Deploying models on Kubernetes

• Using KServe for scalable inference

• Hosting models with AWS SageMaker

MLOps Pipelines & Automation

• Building automated ML pipelines

• Managing model promotion and rollback

• Integrating CI/CD concepts into ML workflows

Cloud-Native ML Systems

• Running ML workloads on AWS

• Scaling inference with Kubernetes

• Managing production ML infrastructure

Who This Course Is Best For

• Data scientists moving into production ML

• ML engineers and MLOps engineers

• Software engineers working with ML teams

• Cloud engineers supporting ML platforms

• Learners preparing for MLOps or ML Engineer roles

Prerequisites

• Basic understanding of machine learning concepts

• Familiarity with Python and ML workflows

• Some exposure to cloud or DevOps concepts is helpful but not mandatory

Practical Value

This course stands out because it:

• Focuses on real production challenges, not toy examples

• Uses industry-standard tools used by ML teams

• Covers both infrastructure and ML workflow design

• Emphasizes scalability, reliability, and monitoring

Learners finish the course with a clear mental model of how ML systems run in production.

Career Relevance

Skills from this course align strongly with roles such as:

• MLOps Engineer

• Machine Learning Engineer

• Applied Data Scientist

• AI Platform Engineer

The tooling and workflows taught are directly applicable to enterprise ML deployments.