Machine Learning Operations (MLOps) for Generative AI
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Training TypeLive Training
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CategoryMachine Learning
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Duration2 Hours
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Rating4.9/5
Course Introduction
About the Course
In this hands-on, live training, participants will explore the intersection of Machine Learning Operations (MLOps) and Generative AI. The session will cover the entire lifecycle of AI models, from data collection and model training to deployment, monitoring, and scaling. We’ll dive into the unique challenges posed by generative models, like GPT and DALL·E, and explore the tools, practices, and frameworks needed to manage these models at scale. Participants will leave with practical knowledge of MLOps techniques and strategies that can be immediately applied to generative AI projects.
Course Objective
Understand the fundamental concepts and importance of MLOps in generative AI.
Learn best practices for deploying, monitoring, and scaling generative AI models.
Gain hands-on experience with MLOps tools for versioning, testing, and automating model pipelines.
Explore the lifecycle of generative AI projects, including data handling, model training, and continuous delivery.
Learn how to implement security, compliance, and ethical considerations in MLOps for generative AI.
Develop strategies for managing model performance, drift, and monitoring in real-time production environments.
Who is the Target Audience?
AI Engineers and Data Scientists
MLOps Engineers
Software Engineers working with AI systems
Machine Learning/AI Researchers
DevOps professionals interested in MLOps
Product Managers and CTOs working with AI-powered products
Basic Knowledge
Basic understanding of machine learning and AI concepts
Familiarity with Python and machine learning frameworks (TensorFlow, PyTorch, etc.)
Basic knowledge of DevOps practices and cloud platforms (AWS, GCP, Azure)
Understanding of software development lifecycle (SDLC) concepts
Available Batches
| 13 Aug 2025 | Wed ( 1 Day ) | Filling Fast02:00 PM - 04:00 PM (Eastern Time) |
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What is MLOps?
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Why MLOps matters for generative AI
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Key challenges in generative AI model lifecycle management
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Data pipeline: Preprocessing, augmentation, and storage for generative models
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Model development: Training, fine-tuning, and versioning
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Model deployment: Continuous integration and continuous delivery (CI/CD) for generative AI models
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Overview of popular MLOps tools: MLflow, Kubeflow, TFX, Metaflow
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Managing experiments and model versioning
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Integrating generative AI models into a CI/CD pipeline
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Performance metrics for generative AI models (e.g., loss, accuracy, quality)
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Handling model drift and continuous retraining
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Scaling generative models in production (GPU/TPU considerations, distributed computing)
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Privacy concerns in generative AI (e.g., data privacy, model explainability)
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Bias and fairness in generative models
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Ensuring compliance with industry regulations (GDPR, HIPAA, etc.)
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Real-life case studies of MLOps in generative AI (e.g., GPT-3/4, image generation models like DALL·E)
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Challenges and lessons learned from scaling generative AI in production
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Future trends in AI and MLOps (e.g., autoML, AI governance)
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Best practices for managing generative AI systems at scale