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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MLOps certification is a valuable credential that validates expertise in deploying and managing machine learning operations. It equips professionals with the skills to streamline the ML lifecycle, automate processes, and ensure reliable, scalable ML systems. With MLOps certification, individuals gain a competitive edge in the rapidly evolving field of AI and data science.

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Prerequisite Certification

  • Familiarity with cloud computing platforms and technologies.
  • Experience with version control systems and DevOps practices.
  • Knowledge of data engineering and pipeline management.
  • Understanding of deployment and monitoring of machine learning models.
  • Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
  • Understanding of CI/CD (Continuous Integration/Continuous Deployment) principles.

Who can pursue this certification?

The Certified MLOps Engineer certification is designed for individuals who work in or aspire to work in the field of MLOps (Machine Learning Operations). MLOps engineers are responsible for deploying, managing, and monitoring machine learning models in production environments.

Data Scientists: Data scientists with experience in developing and training machine learning models can benefit from obtaining MLOps skills to effectively deploy and manage their models in production.

Machine Learning Engineers: Machine learning engineers who focus on developing and implementing machine learning algorithms can enhance their expertise by acquiring MLOps knowledge to ensure smooth deployment and operations of the models.

DevOps Engineers: DevOps professionals who are responsible for managing the software development and deployment lifecycle can pursue MLOps certification to specialize in deploying and managing machine learning models.

Software Engineers: Software engineers interested in working with machine learning models can pursue the Certified MLOps Engineer certification to gain the necessary skills to integrate ML models into their software applications.

IT Professionals: IT professionals with a background in data science, machine learning, or software development can expand their skill set by obtaining MLOps certification, enabling them to support machine learning projects within their organization.

Technical Managers and Team Leads: Managers and team leads overseeing machine learning projects can benefit from MLOps certification to understand the best practices and methodologies for deploying and managing ML models, enabling them to effectively guide their teams.

Certified MLOps Engineer

Test Perparation Study Guide

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.

Specialization

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.

COURSE SYLLABUS

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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