The MLOps Certified Professional (MLOCP) certification is designed for machine learning engineers, data scientists, and platform engineers who want to master the operational practices that make ML models reliable, scalable, and maintainable in production. This 100-hour program covers the complete MLOps lifecycle — from experiment tracking and feature engineering to automated ML pipelines, model serving, CI/CD for machine learning, and production monitoring for drift and performance degradation. Participants gain hands-on expertise with 20+ industry-standard MLOps tools including MLflow, Feast, BentoML, Seldon, Kubeflow, ZenML, Evidently, and Grafana.
The MLOps Certified Professional certification validates an individual's capability to operationalize machine learning systems at scale. Going beyond model building, MLOCP holders understand how to design reproducible training pipelines, manage feature stores for consistent feature serving, implement model versioning and registry workflows with MLflow, automate end-to-end ML pipelines using Kubeflow Pipelines or ZenML, and deploy models with production-grade serving frameworks. The certification also covers the critical post-deployment phase: monitoring for data drift and model performance degradation using Evidently and Grafana, enabling teams to detect and remediate model quality issues before they impact business outcomes.
This program is designed for machine learning engineers, data scientists transitioning to production ML roles, MLOps engineers, and platform engineers building ML infrastructure. It also benefits DevOps engineers upskilling in ML systems, data engineering teams supporting ML workflows, and technical leads responsible for ML model quality and reliability in production. Prior experience with Python and basic machine learning concepts (training, evaluation, supervised learning) is required.
Participants complete 3 real-time capstone projects: (1) building a full MLflow experiment tracking system with model registry and automated promotion; (2) creating a Feast feature store with an integrated Kubeflow training pipeline and BentoML serving endpoint; (3) implementing a production monitoring system using Evidently drift reports wired to Grafana dashboards with automated retraining triggers. All projects simulate production ML engineering scenarios across diverse industry verticals.
As part of this program, you will receive a complete MLOps interview preparation kit — crafted from 200+ years of combined industry experience and insights from thousands of DevOpsSupport learners worldwide. The kit covers ML system design questions, MLflow and feature store configuration scenarios, model serving architecture interviews, and behavioral interview guides for ML engineering and MLOps platform roles.
MLOCP validates expertise in operationalizing machine learning systems in production — covering experiment tracking, feature stores, pipeline automation, model serving, CI/CD for ML, and production monitoring for drift and performance degradation.
Ideal for machine learning engineers, data scientists moving into production ML roles, MLOps platform engineers, and DevOps engineers upskilling in ML systems. Prior Python programming and basic ML knowledge are required to benefit fully from the program.
The course covers MLflow, Feast, Kubeflow Pipelines, ZenML, BentoML, Seldon Core, GitHub Actions, Jenkins, Evidently AI, Grafana, and cloud ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML) — over 20 tools in total.
The program spans 100 hours of structured training, typically delivered at 4 hours per day over 25 days. Flexible scheduling and self-paced video access are available for learners who need to balance training with work commitments.
Participants should have Python programming proficiency, experience training ML models (scikit-learn, PyTorch, or TensorFlow), and familiarity with Docker and basic Linux commands. Experience with any ML framework is acceptable — the focus is on operationalization, not model building.
The exam includes multiple-choice questions, scenario-based questions, and practical lab exercises covering all 8 modules. Candidates are assessed on their ability to design, implement, and troubleshoot MLOps systems including pipelines, serving infrastructure, and monitoring setups.
The certification is valid for 3 years. As the MLOps tooling landscape evolves rapidly, recertification ensures your skills remain current. A recertification pathway is available at a reduced cost with an updated exam covering new tools and practices.
MLOCP supports roles including MLOps Engineer, ML Platform Engineer, Machine Learning Engineer, Senior Data Scientist, ML Infrastructure Lead, and AI/ML Technical Lead. It is increasingly required for senior ML engineering positions at technology companies and enterprises building AI products.
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