Certified MLOps Professional
The Certified MLOps Professional program is globally designed to develop expertise in machine learning operations, enabling professionals to manage the ML lifecycle, automate model deployment, and ensure scalable, reliable AI systems in production environments.
Learn directly from global practitioners, MLOps and AI engineering experts, and industry leaders who are shaping the future of scalable machine learning, AI operations, and intelligent automation.
What Sets Our Program Apart?
- Globally Recognized Certification with 2 Exam Attempts
- E-Learning Library Access, Ebook
- LinkedIn Enhancer & Professional Resume Builder
- Capstone Projects
- Generative AI Interview Practice Platform
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About MLOPS Certification
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
1 MLOps Fundamentals+
•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.
2 Experiment Tracking+
•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.
3 Model Registry+
•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.
4 Containerization+
•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.
5 Container Automation+
•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.
6 CI/CD Workflows+
•Design CI/CD pipelines for ML projects.
•Automate testing, training, and deployment.
•Use GitHub Actions to implement CI/CD.
•Manage environment and dependency workflows.
7 TFX Pipelines+
•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.
8 TFX Integration+
•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.
9 Model Serving+
•Learn different serving paradigms (REST, gRPC).
•Use FastAPI and TorchServe for deployment.
•Understand scaling and inference performance.
•Implement secure and responsive APIs.
10 Monitoring Basics+
•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.
11 Kubernetes Deployment+
•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.
12 LLMOps Foundations+
•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.
13 Prompt Engineering+
•Understand prompt types (zero-shot, few-shot).
•Tune prompts for performance and consistency.
•Use prompt chaining and templating strategies.
•Evaluate and debug LLM outputs.
14 RAG Pipeline Basics+
•Learn Retrieval-Augmented Generation concepts.
•Use vector databases like FAISS for document retrieval.
•Build basic RAG pipelines with LangChain.
•Manage sources and indexing strategies.
15 RAG Pipeline Expansion+
•Improve RAG systems with chunking and embeddings.
•Apply advanced filtering and ranking.
•Integrate external tools and data flows.
•Refine document ingestion and pipeline stages.
16 RAG Pipeline Deployment+
•Prepare RAG systems for scalable deployment.
•Use containerization and orchestration tools.
•Optimize retrieval and generation latency.
•Monitor serving infrastructure for reliability.
17 LLM Deployment+
•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.
18 AgentOps Basics+
•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.
19 AgentOps Advanced+
•Build multi-agent systems with communication protocols.
•Use LangGraph to design workflows and decision trees.
•Coordinate complex agent interactions.
•Debug and evaluate agent outcomes.
20 AgentOps + MCP+
•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.
Learn from Experts
Learn from experienced practitioners and industry leaders who bring real-world expertise and practical insights to the program.
Antonio Grasso
SIEMENS AG INFLUENCER
INTEL SOFTWARE INNOVATOR
Shameer Thaha
ACCUBITS (MENA)
CEO
Harinder Seera
OZPERF
CTO, PERFORMANCE TEST CONSULTANT, SPEAKER
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1 Certificate Programs
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3 Certificate Programs
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Target Audience For Certified MLOps
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
5 Years
Complimentary Retake
Yes
Sample Certification
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.