Deployment of ML Models
Building a model is only the first step toward real-world impact. It teaches model serving, API integration, and cloud deployment strategies. Learners explore containerization and scalable inference systems. Monitoring performance and updating models in production is emphasized. Deployment skills ensure AI solutions reach end users effectively. This bridges research and industry practice.
Deployment Elements:
- Production integration
- Scalability and monitoring
- Cloud-based deployment
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Deployment of ML Models Conference Speakers
Recommended Sessions
- Capstone Project
- Classification Algorithms and Decision Systems
- Clustering and Unsupervised Pattern Discovery
- Convolutional Neural Networks (CNNs)
- Data Preparation and Feature Processing
- Deployment of ML Models
- Dimensionality Reduction and Feature Selection
- Ensemble Learning Approaches
- Ethics and Responsible AI
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- Feature Engineering and Representation Design
- Foundations of Machine Learning
- Generative Models and Creativity in AI
- Introduction to Deep Learning
- Model Evaluation and Performance Validation
- Natural Language Processing Basics
- Neural Network Architecture and Optimization
- Optimization and Hyperparameter Tuning
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- Regression-Based Learning Models
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