Certified MLOps Professional

Certified MLOps Professional based on ML lifecycle, CI/CD, deployment, monitoring, versioning, and cloud integration.

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What Sets Our Program Apart?

  •    E-Learning Library Access
  •    Expertly crafted BOK with ready-to-implement resources
  •    Lifetime Valid Certification with 2 Exam Attempts
  •    Capstone Projects
  •    Generative AI Interview Practice Platform
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About MLOPS Certification

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Objectives of MLOps certification

  • Implement ready-to-use MLOps frameworks and templates
  • Understand core principles of MLOps practices
  • Learn ML lifecycle management and automation
  • Apply CI/CD techniques to ML pipelines
  • Align ML workflows with business goals
  • Use templates crafted by industry experts
  • Learn from practical MLOps use case studies
  • Improve collaboration between ML and DevOps teams
  • Identify bottlenecks in model deployment pipelines
  • Ensure scalable and reproducible ML model delivery

Benefits of MLOps certificate

  • Accelerate deployment with expert-built templates
  • Gain skills through real-world MLOps scenarios
  • Stand out in ML engineering roles
  • Optimize pipelines using proven frameworks
  • Master automation for model lifecycle management
  • Showcase readiness for enterprise ML projects
  • Access plug-and-play MLOps blueprints
  • Solve workflow issues with practical insights
  • Prepare for cross-functional ML team roles
  • Apply case-driven strategies in production environments

Exam Syllabus Of MLOps Certification

  • Understand the MLOps lifecycle and key principles.
  • Compare MLOps with traditional DevOps workflows.
  • Learn goals of production ML systems.
  • Introduction to tools like MLflow, Docker, and GitHub Actions.

  • Explore techniques for tracking ML experiments.
  • Learn the role of reproducibility in model development.
  • Use MLflow to manage and compare runs.
  • Integrate experiment tracking into CI/CD workflows.

  • Understand model versioning and model lifecycle stages.
  • Learn how to manage metadata and lineage.
  • Use MLflow Model Registry to organize models.
  • Handle staging, production, and archival transitions.

  • Grasp the fundamentals of Docker for ML workloads.
  • Learn how to design efficient container images.
  • Package ML models and inference services.
  • Build and test reproducible containers.

  • Automate container orchestration with Docker Compose.
  • Manage container lifecycles in ML pipelines.
  • Version and track container-based deployments.
  • Understand Compose file structures and services linking.

  • Design CI/CD pipelines for ML projects.
  • Automate testing, training, and deployment.
  • Use GitHub Actions to implement CI/CD.
  • Manage environment and dependency workflows.

  • Understand the components of TensorFlow Extended (TFX).
  • Build scalable ML pipelines for production use.
  • Integrate data validation and model evaluation.
  • Use TFX for structured pipeline creation.

  • Connect TFX with tools like MLflow.
  • Enhance traceability and observability in pipelines.
  • Register models from TFX in a centralized registry.
  • Maintain end-to-end metadata for pipeline stages.

  • Learn different serving paradigms (REST, gRPC).
  • Use FastAPI and TorchServe for deployment.
  • Understand scaling and inference performance.
  • Implement secure and responsive APIs.

  • Monitor key ML metrics: latency, drift, accuracy.
  • Use Prometheus and Grafana for live dashboards.
  • Detect anomalies in model behavior.
  • Set up alerting and feedback loops.

  • Learn core Kubernetes concepts (Pods, Services, Ingress).
  • Deploy containerized ML models on Kubernetes.
  • Scale services dynamically using K8s features.
  • Use Helm or Kustomize for deployment management.

  • Explore the lifecycle of LLM-based applications.
  • Compare traditional MLOps with LLMOps workflows.
  • Learn foundational tooling for LLMOps.
  • Set up basic LLM environments using LangChain and Hugging Face.

  • Understand prompt types (zero-shot, few-shot).
  • Tune prompts for performance and consistency.
  • Use prompt chaining and templating strategies.
  • Evaluate and debug LLM outputs.

  • Learn Retrieval-Augmented Generation concepts.
  • Use vector databases like FAISS for document retrieval.
  • Build basic RAG pipelines with LangChain.
  • Manage sources and indexing strategies.

  • Improve RAG systems with chunking and embeddings.
  • Apply advanced filtering and ranking.
  • Integrate external tools and data flows.
  • Refine document ingestion and pipeline stages.

  • Prepare RAG systems for scalable deployment.
  • Use containerization and orchestration tools.
  • Optimize retrieval and generation latency.
  • Monitor serving infrastructure for reliability.

  • Evaluate trade-offs of open-source vs API LLMs.
  • Optimize costs, latency, and throughput.
  • Deploy models using vLLM, TGI, or API endpoints.
  • Secure and manage access to LLM endpoints.

  • Understand LLM-powered agents and their architecture.
  • Learn about agent memory and planning.
  • Use LangChain to implement basic agents.
  • Integrate tools and data sources with agents.

  • Build multi-agent systems with communication protocols.
  • Use LangGraph to design workflows and decision trees.
  • Coordinate complex agent interactions.
  • Debug and evaluate agent outcomes.

  • Implement AgentOps using MCP (Multi-agent Communication Protocol).
  • Manage agent context and role definition.
  • Use Streamlit to build interactive agent UIs.
  • Orchestrate agents in production-grade workflows.

Meet our Advisor

Antonio Grasso
Antonio Grasso

Intel Software Innovator

Shameer Thaha
Shameer Thaha

CEO

Harinder Seera
Harinder Seera

CTO, Performance Test Consultant, Speaker

Enrollment Options

Resources Provided by GSDC

Single Certification Module

Unlock full access to all comprehensive resources and earn Global certification

$ 800.0 $ 400.0

Bundle Certification Module

Get 3 certifications at a discounted price for maximum value

$ 1200.0 $ 600.0

Certification Programs

1

Learning Resources

Certification Exam with Free Retake & Practice Exams

Capstone Project, AI Interview Platform & AI Tools Use Cases

Cost Savings (X% Off vs Single Purchase)

50%

Upto 80%

GSDC Membership worth $109 free

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Looking to enroll your employees into this program?

Target Audience For Certified MLOps

  Machine Learning Engineers
  Data Scientists
  DevOps Engineers
  Data Engineers
  AI/ML Project Managers
  Cloud Infrastructure Engineers
  Automation Engineers
  Software Engineers working with ML models
  AI/ML Consultants
  Technical Leads in AI/ML teams

Pre-Requsites for ML Ops Certification

Prior knowledge or hands-on experience in machine learning or DevOps is recommended but not mandatory for attempting the GSDC Certified MLOps Professional (CMLOP) certification.

Exam Details Of mlops Certification

Exam Questions

40

Exam Format

Multiple choice

Language

English

Passing Score

65%

Duration

90 min

Open Book

No

Certification Validity

Lifetime

Complimentary Retake

Yes

Sample of MLOps Professional Certificate

Certified MLOps Professional

Certified MLOps Professional

The GSDC Certified MLOps Professional (CMLOP) is a globally recognized MLOps certification designed for professionals looking to validate their skills in managing and automating machine learning workflows. Where deploying and maintaining ML models efficiently is critical, earning an ML Ops certification proves your ability to bridge the gap between data science and operations.


This MLOps certificate is ideal for data engineers, ML engineers, and DevOps professionals who want to stand out in a competitive job market. The certification focuses on real-world relevance, helping you demonstrate expertise in key MLOps tools and practices. While GSDC does not provide training, you'll receive access to ready-to-implement hands-on resources, templates, and supporting materials that can accelerate your learning. Whether you're aiming for better career opportunities or want to future-proof your skills, this MLOps certification equips you with a recognized edge.


If you're already exploring ML Ops certification options or researching MLOps certifications, this one delivers practical value and global recognition.


Frequently Asked Questions

This certification is ideal for Machine Learning Engineers, Data Scientists, DevOps Engineers, Data Engineers, AI/ML Consultants, and professionals involved in deploying or managing ML workflows.

CMLOP is a certification that validates your ability to integrate machine learning with DevOps practices for efficient model deployment and lifecycle management. Benefits include job-ready skills, access to practical use case studies, and expert-crafted, ready-to-implement templates for real-world application.

Earning the CMLOP certification can open up roles such as MLOps Engineer, ML Infrastructure Engineer, AI/ML Deployment Specialist, and Technical Lead in AI/ML projects, all of which are in high demand across industries.

As organizations increasingly operationalize AI, MLOps is becoming essential for scalable, reliable ML deployments. This certification proves your readiness to meet these industry needs with real-world implementation strategies and tools.

The CMLOP certification is valid for a lifetime, with no expiration or renewal required.

Organizations benefit from having certified professionals who can streamline ML operations, reduce deployment time, improve model reliability, and implement scalable MLOps practices using proven templates and case-based solutions.

Why GSDC ?

The Global Skill Development Council (GSDC) is an independent, vendor-neutral, international credentialing and certification organization for the emerging technologies:

  • Advisory board members and SMEs are from around the world, drawn from different specializations.

  • Supported by the world's most esteemed thought leaders from Yale, MIT, Stanford, Wharton, and Harvard.

  • Hub of Trending Technologies and framework certifications.

  • Content curated by Industry's best Subject matter experts.

  • Webinars and Conferences.

  • Training Partners Across The Globe.

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