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Launch Your Career in MLOps Engineering
A model in a notebook earns nothing. This program teaches you to package, deploy, monitor and retrain models in production - the work that turns machine learning into a running service.
Global #1 Training & Internship Program
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Program Summary
MLOps is the discipline of getting machine learning models into production and keeping them healthy there. This program builds a practical foundation in that work: how a model is packaged and versioned, how training and deployment pipelines are automated, how a served model is monitored for drift and degradation, and how retraining is triggered and validated without breaking what already works.
Alongside the technical modules, the program includes hands-on labs and a virtual internship. You will containerise a model, build a CI pipeline that tests and deploys it, serve it behind an API, instrument drift monitoring, and finish with project work you can show to an employer.
Why Refonte Learning's MLOps Engineer Course is the Best Fit for You
- Concrete Projects, Real-World Experience
- In-Depth Skill Enhancement
- Seasoned Guidance
- Potential Internship
Program Specifics
Requirement
Engaged in bachelor's or postgraduate studies in Aerospace, Electrical Engineering, Physics, Computer Science, or related fields
Period
3 months
Dedication time
10-12 Hours/Week
Career Result
MLOps Engineer, Machine Learning Engineer, ML Platform Engineer, Model Deployment Engineer and Data Platform Engineer roles across technology, finance, healthcare, retail and industrial companies.
Competencies you'll develop
- ML Lifecycle & Reproducibility
- Experiment Tracking & Model Registry
- Containerisation & Environment Parity
- Training & Deployment Pipelines (CI/CD)
- Model Serving, Batch & Real-Time
- Feature Stores & Data Versioning
- Monitoring, Drift & Data Quality
- Retraining Triggers & Safe Rollout
- Cost, Latency & Resource Management
Expertise Featured
- ML Lifecycle & Reproducibility
- Experiment Tracking & Model Registry
- Containerisation & Environment Parity
- Training & Deployment Pipelines (CI/CD)
- Model Serving, Batch & Real-Time
- Feature Stores & Data Versioning
- Monitoring, Drift & Data Quality
- Retraining Triggers & Safe Rollout
- Cost, Latency & Resource Management
Tools Taught

Educational Mentors

PhD Matthias Schmidt
Department of Data Engineering
Leveraging his deep-rooted knowledge as a senior data engineer, Matthias Schmidt plays a pivotal role in shaping our Data Engineering Training & Internship program. With a remarkable 16-year background in computer science, he excels in diverse domains such as sophisticated regression analysis, algorithmic design for enhancing customer loyalty, financial econometrics, and quantitative risk forecasting. His proficiency extends to harnessing big data solutions tailored for banking and financial services, making him an invaluable mentor in the field of data engineering.
Your Educational Path
Take a model out of a notebook into a versioned, containerised service with an API, and make the training run reproducible by someone else.
Build a CI pipeline that tests data and model quality before deployment, so a bad model fails the build rather than the customer.
Instrument a served model for data and prediction drift, then decide - with evidence - when it actually needs retraining.
Need clarification about the course? Get in touch with us
Application DETAILS
Enroll today with just three easy steps and dive into our rich learning opportunities to start your educational journey.
Step 1
Sign Up
Step 2
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Step 3
Kickstart your learning
Admission Prerequisites
Obligatory: Working towards a bachelor's or higher-level degree
We delight in recognizing your successes
Upon successful completion of the program, Refonte Learning offers two certificates: a Training Certificate and a Certificate of Internship. Students who demonstrate outstanding performance may receive a Letter of Recommendation and a Certificate of Appreciation for their achievements. In addition, top performers will be rewarded with exclusive prizes such as Amazon vouchers, gift hampers, and personalized T-shirts.
Advantages of the Program
- Concrete Projects, Real-World Experience
- In-Depth Skill Enhancement
- Potential Futures

Application Fees
We believe in making our programs accessible to everyone. That's why we offer a variety of financing options, including competitive rates as low as 0% interest and no hidden costs. You can also choose to make a convenient one-time payment.
Equated Monthly Installment payment options
Explore budget-friendly EMI plans with reputable finance firms. Overcome financial obstacles and stay focused on your personal development.
Credit/Debit Card and Paypal
Costs as low as
Installment I: USD 204
Installment II: USD 98
One-Time Payment Options
Experience the ease and flexibility of a single payment, allowing you to start your career journey without any hassle.
Credit/Debit Card and Paypal / Online Banking
Total Enrollment Costs
USD 300
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Frequently Asked Questions
What career opportunities does this course provide?
MLOps Engineer, Machine Learning Engineer and ML Platform Engineer roles. Sampled US postings ranged $104K-$190K at employers including AgileEngine and CliftonLarsonAllen.
How is this different from a data science course?
Data science produces the model; this program is about everything after that - packaging, deploying, serving, monitoring and retraining it.
Do I need a GPU or a cloud account?
No. Labs run on small models, local Docker and free cloud tiers.
Is this course suitable for beginners?
It suits learners with Python and some basic machine learning. No infrastructure background is needed.
Can I take this course while working full-time?
Yes. It requires 10-12 hours per week and sessions are recorded.
Ready to fulfill your dream of becoming a Professional MLOps Engineer?
Begin your journey with Refonte International's Training & Internship Program for comprehensive training, valuable certificates, and exciting future opportunities.






















































































