Certified MLOps Engineer Certification (CMOE-DS2160)

  • Credential of Expertise: MLOps certification validates proficiency in deploying and managing machine learning operations, showcasing expertise.
  • Process Automation Skills: Certified individuals possess the skills to automate processes, ensuring the reliability and scalability of machine learning systems.
  • Competitive Edge in AI: MLOps certification provides a competitive advantage in the rapidly evolving fields of AI and data science, reflecting a commitment to excellence in navigating industry challenges.

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Introducing the MLOps Certification, a key credential propelling individuals toward success in the dynamic field of machine learning operations (MLOps). This certification imparts essential expertise, enabling professionals to excel in the ever-evolving domain of MLOps engineering.

In response to the growing demand for proficient MLOps professionals, the MLOps Certification provides a distinct advantage in today's  job market. Emphasizing practical application and industry-relevant skills, this certification effectively equips individuals to thrive as accomplished MLOps engineers. Covering a broad spectrum of topics critical to MLOps success, it addresses the implementation and management of machine learning models in real-world production environments.

IABAC's MLOps certification imparts knowledge of best practices and industry-standard tools, including Docker, Kubernetes, Git, and cloud platforms like AWS, Azure, or Google Cloud. This comprehensive training ensures that Certified MLOps Engineers are well-prepared to overcome challenges associated with deploying and managing machine learning models at scale.

The MLOps Certification extends beyond technical prowess, fostering vital communication and collaboration abilities essential for effective teamwork within cross-functional environments. MLOps engineers, crucial in bridging the gap between model development and deployment, collaborate closely with data scientists, software engineers, and DevOps teams. This certification ensures individuals possess the requisite skills to facilitate seamless collaboration and drive successful MLOps implementations.

Given the rapid advancements in AI and machine learning technologies, the demand for skilled MLOps professionals is poised to grow further. Stay ahead of the curve and position yourself as a leader in this transformative field by obtaining the MLOps Certification.

By attaining the MLOps Certification, you'll unlock a wealth of career opportunities and become a key driver of innovation in AI and machine learning operations across various industries.


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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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What is a Certified MLOps Engineer?

A Certified MLOps Engineer is a professional skilled in deploying, monitoring, and managing machine learning models in production environments, ensuring seamless integration between data science and operations.

What are the key responsibilities of a Certified MLOps Engineer?

Responsibilities include automating model deployment, monitoring model performance, managing model versioning, optimizing infrastructure for machine learning workflows, and collaborating with data scientists and IT teams.

Why is MLOps important?

MLOps ensures the efficient and reliable deployment of machine learning models at scale, leading to faster time-to-market, improved model performance, reduced operational costs, and enhanced collaboration between data science and IT teams.

What skills are required to become a Certified MLOps Engineer?

Skills include proficiency in machine learning algorithms, and programming languages like Python, knowledge of DevOps practices, familiarity with cloud platforms like AWS or Azure, expertise in containerization technologies such as Docker, and experience with orchestration tools like Kubernetes.

What are the benefits of obtaining MLOps certification?

Certification validates expertise in MLOps practices and technologies, enhancing career prospects, increasing job opportunities, and demonstrating credibility to employers and clients in the field of machine learning operations.