MLOps Essentials Training is a practical two-day program designed to help professionals understand how machine learning models are operationalized, managed, and scaled in real-world environments. This course focuses on the end-to-end MLOps lifecycle including model development handoff, deployment, monitoring, governance, and continuous improvement, enabling participants to bridge the gap between data science and production-ready machine learning systems.

• Understand MLOps fundamentals
• Learn ML lifecycle management concepts
• Understand model deployment workflows
• Gain knowledge of CI/CD for ML basics
• Learn model monitoring techniques
• Understand data and pipeline automation
• Explore scalable ML operations concepts
• Identify enterprise MLOps use cases

• Data scientists and machine learning practitioners
• ML engineers and AI developers
• DevOps and platform engineers
• Technology and digital transformation professionals
• Product and engineering managers working with ML systems

• Basic understanding of machine learning or data science concepts
• Familiarity with software development or IT systems is beneficial
• Awareness of cloud or deployment environments is helpful
• No advanced DevOps or programming background required

• Understanding the MLOps lifecycle and workflows
• Deploying and managing machine learning models responsibly
• Monitoring model performance and data drift
• Applying versioning, governance, and collaboration practices
• Supporting scalable and reliable ML systems

By the end of this training, participants will be able to explain MLOps concepts clearly, understand how machine learning models are deployed and maintained in production, apply monitoring and governance practices, and support scalable, reliable, and responsible machine learning operations within their organizations.

Introduction to MLOps and ML Lifecycle
• What MLOps is and why it matters
• Difference between ML development and production ML
• Challenges of deploying ML models
• Overview of end-to-end ML lifecycle

Model Deployment and Versioning Concepts
• Model packaging and deployment approaches
• Model versioning and experiment tracking
• Managing datasets and feature versions
• Collaboration between data science and engineering teams

CI/CD Concepts for Machine Learning
• CI/CD principles applied to ML workflows
• Automating training and deployment pipelines
• Testing models and pipelines
• Managing changes and rollbacks

Monitoring, Drift, and Model Performance
• Monitoring predictions and performance metrics
• Detecting data drift and concept drift
• Handling model degradation
• Retraining and continuous improvement strategies

Governance, Security, and Responsible MLOps
• Model governance and auditability
• Security and access control for ML systems
• Bias, fairness, and compliance considerations
• Responsible and ethical ML operations

Scaling MLOps and Organizational Adoption
• Scaling ML systems across teams and environments
• Tooling and platform considerations
• Measuring business impact and ROI
• Building an MLOps roadmap

• MLOps fundamentals
• ML lifecycle management
• Model deployment concepts
• CI/CD for machine learning
• Data pipeline automation
• Model monitoring techniques
• Version control and collaboration
• ML infrastructure basics
• Security and governance considerations
• Practical MLOps scenarios

• MLOps workflow and lifecycle assessment
• Model deployment and monitoring scenario discussion
• Governance and drift management exercise
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in MLOps Essentials Training, validating their expertise in understanding MLOps concepts, model lifecycle management, monitoring, governance, and responsible ML operations.