This is the MLOps reality check most data scientists face. According to Gartner, 87% of AI projects never make it to production. Why? Because building a model is only 10% of the battle. The rest? Deployment, monitoring, scaling, and governance — the stuff no one teaches in Kaggle tutorials.
That’s exactly where the MLOps Foundation Certification from DevOpsSchool steps in. This isn’t a 100-hour PhD in pipelines. It’s a focused, practical, 25–30 hour bootcamp that gives you the core MLOps skills to go from notebook to production — without the guesswork.
What “Foundation” Really Means
The MLOps Foundation Certification is built for beginners with ambition. You don’t need to be a Kubernetes wizard or a CI/CD ninja. If you can train a model in Python and use Git, you’re ready.
This live online training runs over 4–6 weeks, with 2–3 hour sessions packed with demos, labs, and Q&A. You’ll follow a real-world ML workflow — from data versioning to model serving — using tools actual companies rely on.
What You’ll Actually Do:
- Version datasets and models like code
- Build a CI/CD pipeline that retrains on new data
- Containerize and deploy a model as a REST API
- Set up basic monitoring for accuracy drop-offs
- Collaborate using MLflow + GitHub + Docker
Tools You’ll Use (No Toy Projects):
| Category | Tools Covered |
|---|---|
| Data | DVC, Delta Lake |
| Experiment Tracking | MLflow |
| Packaging | Docker, Conda |
| Orchestration | GitHub Actions, Airflow (intro) |
| Serving | FastAPI, BentoML |
| Monitoring | Prometheus + Grafana (basics) |
No “Hello World” fluff. Every lab builds toward a capstone project: a working image classification API with automated retraining and health checks.
Who Should Enroll? (Spoiler: Probably You)
This MLOps training is perfect if you’re:
- A data scientist tired of handing models to “someone else”
- A software engineer moving into ML infrastructure
- A DevOps pro adding AI to your stack
- A student or recent grad targeting MLOps engineer roles
- A team lead standardizing ML workflows
- Part of a corporate team (get 25% off for 7+ members)
No prior DevOps experience required. We start with Git basics and scale up smoothly.
Learning Outcomes: What You’ll Be Able to Say “I Did This”
By the end of this MLOps certification course, you’ll confidently:
- Version data and models using DVC and MLflow (no more “where’s the dataset?”)
- Automate model training with GitHub Actions when new data lands
- Package and deploy models as Dockerized APIs in under 10 minutes
- Monitor model performance and get alerts when accuracy drifts
- Explain MLOps principles in interviews (and back it up with code)
- Earn two certifications: MLOps Foundation + DevOpsSchool Mastery Badge
Here’s your module roadmap — clear, visual, and exam-ready:
Table 1: MLOps Foundation Certification – Module Breakdown
| Module | Duration | Key Skills | Hands-On Lab |
|---|---|---|---|
| 1. MLOps 101 | 3 hrs | Why MLOps? Levels 0–2 | ML lifecycle mapping |
| 2. Data & Code Versioning | 5 hrs | Git + DVC | Track a 10GB dataset |
| 3. Experiment Tracking | 4 hrs | MLflow UI & API | Log 50+ runs |
| 4. Model Packaging | 4 hrs | Docker, ONNX | Build a 100MB image |
| 5. CI/CD for ML | 5 hrs | GitHub Actions | Auto-retrain on push |
| 6. Model Serving | 4 hrs | FastAPI + BentoML | Deploy to localhost |
| 7. Monitoring Basics | 3 hrs | Prometheus alerts | Accuracy drop dashboard |
| Capstone Project | 2 hrs review | End-to-end pipeline | Live demo + feedback |
Why DevOpsSchool? Because Not All Training Is Equal
DevOpsSchool.com isn’t just another e-learning portal. It’s a global leader in DevOps, Cloud, and emerging tech certifications — trusted by 8,000+ professionals across 50+ countries. We don’t do pre-recorded monotony. We do live, interactive, mentor-driven learning.
And the mentor? Rajesh Kumar.
- 20+ years building production systems at scale
- Trained teams at Fortune 500s, startups, and governments
- Founder of Rajesh Kumar — a DevOps knowledge hub with 100K+ monthly readers
- Known for breaking down complex tools into “aha” moments
“Rajesh doesn’t just teach DVC — he shows you how to recover a lost dataset at 3 AM when prod is down.” – MLOps Foundation Batch 8
You also get:
- Lifetime access to labs, recordings, and updates
- Private Slack community for doubt-clearing
- Resume-ready GitHub repo from your capstone
- Mock interviews with real-world scenarios
Career Benefits: From “I Tried ML” to “I Ship ML”
The MLOps job market is on fire. LinkedIn reports MLOps roles grew 9.8x since 2020. Companies aren’t just hiring data scientists — they want engineers who can deploy.
Table 2: MLOps Foundation vs. Self-Learning – What You Gain
| Benefit | MLOps Foundation Certification | Self-Learning (YouTube/Blogs) |
|---|---|---|
| Structured Path | Yes, 7 modules + capstone | Random videos |
| Live Mentor Feedback | Yes, Rajesh Kumar | None |
| Job-Ready Portfolio | Yes, GitHub + Docker | Rarely |
| Interview Prep Kit | Yes, 150+ questions | Generic |
| Certification | Yes, Dual (Foundation + Mastery) | None |
| Time to Deploy First Model | 3 weeks | 3–6 months |
| Salary Boost Potential | +25–40% within 12 months | Slow or none |
Real Outcomes:
- Junior MLOps Engineer: ₹12–20 LPA (India) | $90K–$120K (USA)
- ML Platform Engineer: ₹18–30 LPA | $130K–$160K
- Promotion Path: Data Scientist → MLOps Engineer → AI Platform Lead
One recent grad landed a role at a fintech unicorn within 2 months of certification. His edge? A live demo of his capstone during the interview.
Ready to Stop Dreaming and Start Deploying?
You don’t need another Kaggle medal. You need code that runs in production.
The MLOps Foundation Certification is your launchpad — practical, proven, and led by someone who’s shipped AI at scale for two decades.
What You Get When You Enroll Today:
- Live training with Rajesh Kumar
- 50+ hands-on labs + capstone
- Lifetime LMS + community access
- Dual certification + interview kit
- Early bird bonus: Free MLflow Mastery Workshop (worth $99)
Take the First Step:
✉️ contact@DevOpsSchool.com
📞 +91 99057 40781 (India)
📞 +1 (469) 756-6329 (USA)
