Instructor-led MLOps Certification live Online Training Schedule

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Price:
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45,000.00
Duration: 60 Hours

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About your MLOps Certification Training

Skills Covered

Tools Covered

MLOps Certification Training Syllabus

Curriculum Designed by Experts

Topics
  • What is MLOps?
  • History and Evolution of MLOps
  • Key Concepts and Terminology
Topics
  • Overview of the Machine Learning Lifecycle
  • Data Preparation and Feature Engineering
  • Model Training and Evaluation
Topics
  • Version Control Systems (Git)
  • Continuous Integration/Continuous Deployment (CI/CD)
  • Containerization (Docker, Kubernetes)
  • Model Serving (TensorFlow Serving, TorchServe)
Topics
  • Setting Up CI/CD Pipelines for ML
  • Automating Data Ingestion and Preparation
  • Model Training Pipelines
  • Model Validation and Testing
Topics
  • Deploying Models to Production
  • Monitoring Model Performance
  • Retraining and Updating Models
  • Handling Model Drift and Data Shifts
Topics
  • Cloud Platforms for MLOps (AWS, Azure, GCP)
  • Cloud-Native MLOps Tools
  • Scalability and Reliability in the Cloud
Topics
  • Ensuring Data Privacy and Security
  • Compliance with Regulations (GDPR, HIPAA)
  • Best Practices for Secure MLOps
Topics
  • Successful MLOps Implementations
  • Lessons Learned from Industry Leaders
  • Future Trends and Innovations in MLOps


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Why MLOps Certification Training From Xpertised Learning

Live Interactive Classes

  • Top-Tier Trainers
  • Real Time Guidance by Industrial Experts
  • In Class Doubt Clearance

Resource Access

  • Life Time Access to Resource Library

Support

  • One-to-One Lab and Training Assistance
  • Help Desk Support
  • Doubt Clearance in Real-time

Project Based Learning

  • Labs and Learning Based from Industrial Experts
  • Learning Based on Project Scenarios

Certification

  • Xpertised Learning Training Certificate
  • Certificate of Completion

Labs

  • Cloud based Labs *Labs based on course requirement only

MLOps Certification Training Details

This comprehensive course offers an in-depth exploration of MLOps, focusing on the intersection of machine learning and operations. Participants will learn how to implement continuous integration and continuous deployment (CI/CD) for machine learning models, manage model performance, and handle the complexities of model deployment and monitoring.

  • Develop expertise in MLOps, enhancing your ability to manage and deploy machine learning models at scale, increasing your value in AI and data science roles.
  • Boost your career prospects by mastering MLOps practices, making you a sought-after candidate for roles in machine learning operations and data engineering.
  • Stay competitive in the tech industry by acquiring skills in MLOps, ensuring your ability to streamline and optimize AI workflows and model deployment.
  • Comprehensive understanding of MLOps concepts and practices.
  • Hands-on experience with real-world projects and applications.
  • Improved ability to deploy and manage ML models effectively.
  • Access to a network of MLOps professionals and experts.
  • Implementing CI/CD for machine learning
  • Deploying and monitoring ML models
  • Managing data and model versioning
  • Ensuring security and compliance in MLOps
  • Git and version control systems
  • Docker and Kubernetes for containerization
  • CI/CD tools (Jenkins, GitLab CI/CD)
  • Cloud platforms (AWS, Azure, GCP)
  • Model serving tools (TensorFlow Serving, TorchServe)
  • Understand the core concepts and techniques of MLOps.
  • Implement CI/CD pipelines for machine learning models.
  • Deploy, monitor, and manage ML models in production.
  • Ensure security and compliance in MLOps practices.
  • Machine Learning Engineers
  • Data Scientists
  • DevOps Engineers
  • Software Developers
  • IT Professionals
  • Anyone interested in integrating ML into production environments
  • Basic knowledge of machine learning and data science
  • Familiarity with programming (Python preferred)
  • Understanding of DevOps practices is a plus
  • Core concepts and techniques of MLOps
  • Practical skills in CI/CD for machine learning
  • Deploying and monitoring ML models
  • Ensuring security and compliance in MLOps
  • A computer with an internet connection
  • Basic programming tools (Python and libraries)
  • Access to cloud platforms (AWS, Azure, GCP)
  • MLOps Engineer
  • Machine Learning Engineer
  • Data Scientist
  • DevOps Engineer
  • IT Professional

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