Become the Engineer Who Makes AI Work in the Real World



While data scientists create models, MLOps engineers build the systems that turn those models into reliable, scalable business solutions. This 8-week intensive program equips you with the specialized engineering skills to deploy and maintain machine learning systems in production environments.

The MLOps Career Advantage

MLOps professionals typically earn 15-25% more than traditional software engineers with similar years of experience. Why? Because they solve the critical "last mile" problem that prevents most AI projects from delivering real business value.

  • Rare Skill Set: Few engineers have the specialized knowledge to bridge ML and production systems
  • Critical Business Need: Companies need MLOps expertise to see ROI on their AI investments
  • Growing Demand: 64% of enterprise AI leaders cite MLOps as their most difficult role to fill

Your Career After This Course:

MLOps Engineer ($150,000 - $195,000)

ML Platform Engineer ($160,000 - $200,000)

ML Infrastructure Architect ($170,000 - $215,000)

ML Reliability Engineer ($155,000 - $190,000)

Salary ranges based on May 2025 industry data for mid-level positions in major tech hubs

What Sets This Program Apart

Production-First Mindset: Focus on real-world implementation challenges, not theoretical concepts

End-to-End Systems: Build complete MLOps pipelines from data ingestion to model monitoring

Hands-On Labs: Eight full-afternoon lab sessions building production-grade ML infrastructure

Capstone Project: Demonstrate your capabilities with a complete MLOps implementation

What Past Students Are Saying

Alex Rivera, MLOps Engineer

"After years as a software engineer, this course helped me specialize in MLOps. I landed a role at a fintech company with a 40% salary increase and much greater career growth potential."

Tasha Williams, ML Platform Engineer

"My company had several ML models that data scientists had built but couldn't deploy reliably. After completing this course, I implemented proper MLOps practices, and we finally started seeing ROI from our AI investments. My value to the organization skyrocketed."

Raj Patel, ML Infrastructure Lead

"This course gave me exactly what I needed to transition from DevOps to MLOps. The hands-on labs dealing with real-world challenges were invaluable. I was promoted within three months of completing the program."

Course Schedule

Next Session Starting On

July 28th 2025, 7PM EST


Your Time Commitment

  • Monday, Wednesday, Friday: 7PM-9PM
  • Saturday: 9AM-12:30PM (with 30-min break)
  •   Plus 5-10 hours of weekly project work



Online Live Instructor Lead Training Session for Job-Ready MLOps Mastery


Curriculum Overview

  Week 1: ML Engineering Foundations
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  Week 2: Data Engineering for ML
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  Week 3: Model Development Lifecycle
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  Week 4: CI/CD for Machine Learning
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  Week 5: Model Deployment
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  Week 6: Production Monitoring
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  Week 7: Advanced MLOps
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  Week 8: Capstone Project
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Choose a Pricing Option

Tuition Investment, ROI, & Timings



Tuition: 

Regular: $1,399 (Payment plans are available upon approval)

Student Price: $999 (Payment plans are available upon approval)

ROI:

Graduates report an average salary increase of $32,000 within six months of completion. Companies typically spend $15,000+ to recruit specialized AI engineers—your new skills make you an immediate asset.

Time Commitment:

  • Monday, Wednesday, Friday: 7PM-9PM
  • Saturday: 9AM-12:30PM (with 30-min break)
  • Plus 5-10 hours of weekly project work

Your Instructor

Dr. Vijay Boppana, Ph.D.

With experience as an AI researcher, engineer, and business consultant, Dr. Boppana specializes in bridging the gap between technical capabilities and business applications. He has advised C-suite executives at Fortune 500 companies on AI strategy and implementation.


Limited Enrollment — Secure Your Spot

This specialized program is limited to 20 participants to ensure hands-on guidance and high-quality learning outcomes. Previous cohorts have filled quickly due to the high demand for MLOps expertise.

Apply now and position yourself at the forefront of AI engineering.

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Frequently Asked Questions

Is this course right for me?

This course is ideal for software engineers, DevOps engineers, and data engineers who want to specialize in machine learning operations. If you already have software engineering experience and want to bridge into ML deployment, this program provides the specialized knowledge you need.

What technical prerequisites are required?

Participants should have:

  • Experience with Python programming
  • Familiarity with cloud services (AWS, GCP, or Azure)
  • Basic understanding of machine learning concepts
  • Experience with Docker containers and CI/CD pipelines

How does this differ from a data science course?

While data science focuses on model development and algorithms, this MLOps course focuses on the engineering required to deploy, scale, and maintain those models in production. We focus on the infrastructure, pipelines, and operational practices that ensure ML systems deliver reliable business value.

Will I receive career support?

Yes! The program includes:

  • Resume review tailored to MLOps roles
  • LinkedIn profile optimization
  • Interview preparation for MLOps positions
  • Access to our employer network of companies seeking MLOps talent