
Online Class Machine Learning on Production Cohort 4
Temen-temen kalau dah tertarik di ML tapi nggak belajar gimana cara kita shipping model AI kita ke production, gass join!!
bonus:
- video based supplementary material (untuk membantu pembelajaran Online Class)
- Online Class Computer Vision with Pytorch
- Online Class NLP with Pytorch
- tugas besar dan dibimbing lansung saat praktik
https://www.rubythalib.com/ml-on-production.html
Start : 4 Agustus , 2024
Setiap Minggu: 08.00-10.00 WIB
What You Get in AI Online Class
- Interactive Class
- Live Question and Answer with Mentor
- e-Certificate
- Portfolio
- Interactive Class
Material:
Session 1 : Introduction to Machine Learning on Production
Overview of Machine Learning (ML) lifecycle
Challenges in deploying ML models to production
Importance of ML in real-world applications
Introduction to production-grade ML systems
Case studies of successful ML deployments
Session 2 : Data Preparation and Preprocessing
Data collection and storage for production systems
Data cleaning and preprocessing techniques
Feature engineering for production environments
Handling missing values and outliers
Data versioning and management strategies
Session 3 : Model Development and Evaluation
Choosing appropriate ML algorithms for production
Model training and evaluation best practices
Hyperparameter tuning for production models
Techniques for model interpretability and explainability
A/B testing and evaluation metrics for production models
Session 4 : Scalable Infrastructure for ML
Introduction to scalable ML infrastructure
Cloud computing platforms for ML in production
Containerization and orchestration (e.g., Docker, Kubernetes)
Scalable data processing frameworks (e.g., Apache Spark)
Managing infrastructure costs and resources
Session 5 : Deployment Strategies
Strategies for deploying ML models in production
Model serving and inference pipelines
Monitoring and logging for deployed models
Continuous integration and continuous deployment (CI/CD)
Blue-green deployments and canary releases
Session 6 : Model Maintenance and Lifecycle Management
Importance of model maintenance in production
Strategies for model retraining and updating
Versioning and rollback mechanisms
Ethical considerations in model lifecycle management
Case studies and real-world examples of model maintenance challenges
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