Certified MLOps Engineer Certification (CMOE-DS2160)

  • MLOps is an intermediate to expert level certification that can be pursued by both working professionals and aspiring beginners.
  • One of the most respected data science certificates based on the EU EDISON framework.
  • The certification programs are aligned with Industry requirements.
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Introducing the Certified MLOps Engineer Certification, your gateway to a thriving career in the realm of machine learning operations (MLOps). This prestigious certification empowers individuals with the essential expertise and know-how to thrive in the ever-evolving domain of MLOps engineering.

In response to the escalating demand for skilled MLOps professionals, the Certified MLOps Engineer Certification offers a distinct advantage in today's competitive job market. By emphasizing practical application and industry-relevant skills, this certification effectively equips individuals to excel as accomplished MLOps engineers.The certification covers a wide range of topics essential for MLOps success, including the implementation and management of machine learning models in real-world production environments. From designing and optimizing MLOps pipelines to ensuring scalability and reliability, participants gain a deep understanding of the intricacies of MLOps engineering.Participants will learn the best practices and industry-standard tools for MLOps, including popular technologies such as Docker, Kubernetes, Git, and cloud platforms like AWS, Azure, or Google Cloud.

This comprehensive training ensures that Certified MLOps Engineers are well-equipped to tackle the challenges associated with deploying and managing machine learning models at scale.Upon completion of the certification, individuals are recognized as Certified MLOps Specialists. This designation demonstrates their expertise and proficiency in the field, making them valuable assets to any organization seeking to leverage machine learning for operational success.

The Certified MLOps Engineer Certification goes beyond technical skills. Participants also develop essential communication and collaboration abilities necessary for effective teamwork within cross-functional environments. MLOps engineers work closely with data scientists, software engineers, and DevOps teams to bridge the gap between model development and deployment. This certification ensures that individuals possess the necessary skills to facilitate seamless collaboration and drive successful MLOps implementations.Earning the Certified MLOps Engineer Certification opens doors to a wide range of career opportunities.

Industries such as technology, finance, healthcare, and e-commerce are actively seeking professionals with MLOps expertise. Organizations understand the value of efficient and reliable machine learning operations, and Certified MLOps Engineers are in high demand to drive these initiatives forward.With the rapid advancements in AI and machine learning technologies, the need for skilled MLOps professionals will only continue to grow. Stay ahead of the curve and position yourself as a leader in this transformative field by obtaining the Certified MLOps Engineer Certification.


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Introduction to MLOps

  • Overview of MLOps and its importance
  • Key principles and best practices in MLOps
  • Roles and responsibilities of an MLOps engineer

Machine Learning Fundamentals

  • Basics of machine learning algorithms and models
  • Data preprocessing and feature engineering techniques
  • Model training, evaluation, and validation
  • Model deployment and serving strategies

Software Engineering for Machine Learning

  • Version control and collaboration in ML projects
  • Containerization and packaging of ML models
  • Reproducible ML experiments and model management
  • Continuous integration and continuous deployment (CI/CD) pipelines for ML

Infrastructure and Deployment

  • Cloud computing platforms for ML deployment
  • Infrastructure as code using tools like Terraform or CloudFormation
  • Scalable and resilient ML infrastructure setup
  • Monitoring and logging for ML models in production

Data Management in MLOps

  • Data versioning and lineage for ML datasets
  • Data quality and validation techniques
  • Data pipelines and workflows for ML data processing
  • Data governance and compliance considerations

Model Monitoring and Governance

  • Monitoring model performance and drift
  • Model explainability and interpretability techniques
  • Model retraining and updating strategies
  • Ensuring model fairness and avoiding bias

Security and Compliance in MLOps

  • Secure data handling and privacy protection
  •  Access control and authentication for ML systems
  • Compliance with regulations (e.g., GDPR, HIPAA)
  • Risk management and mitigation in MLOps

DevOps Integration and Collaboration

  • Collaboration between data scientists and software engineers
  • Integration of ML workflows into existing DevOps processes
  • Continuous monitoring and feedback loops
  • Agile methodologies for MLOps projects

MLOps Tools and Technologies

  • Frameworks for end-to-end MLOps (e.g., Kubeflow, MLflow)
  • Infrastructure and deployment tools (e.g., Kubernetes, Docker)
  • CI/CD tools and practices for ML (e.g., Jenkins, GitLab)
  • Monitoring and observability tools for ML systems (e.g., Prometheus, Grafana)

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Certified MLOps Engineer

(Test Preparation Study Guide)

Discover the Certified MLops Engineer Certification and access a free study guide to master the essential skills in managing machine learning operations efficiently. Start your journey today


The Benefits

International Credential

IABAC is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

Global Opportunities

IABAC certification provides global recognition of the relevant skills, thereby opening opportunities across the world.


IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

Relevant and updated

IABAC CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

Higher Salaries

On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

Summits & Webinars

In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.

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Is the certification open to individuals who have a gap after graduation ?

 Yes , you can still opt for this certification which might be helpful for your future career journey

What Is Unique about the IABAC Certification Body?

IABAC is a globally recognized professional association dedicated to growing and enhancing the field of applied data science and business analytics

How long does it take to get a Certified MLOps Engineer Certification after the exam completion?

The time taken for Certified MLOps Engineer Certification after the exam completion is usually 10 working days.