AI and Machine Learning Deployment to Production 6 Sections19 LessonsLifetime Access
Curriculum
- 6 Sections
- 19 Lessons
- Lifetime Access
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- Model Selection4 Items
- 1.1Explaining K-Fold Cross-Validation Techniques: Mastering Model Evaluation
- 1.2Mastering the Bias-Variance Tradeoff in Machine Learning Models
- 1.3Improving Machine Learning Model Performance with K-Fold Cross-Validation in Python
- 1.4Optimizing SVM Models with GridSearchCV in a Step-by-Step Python Tutorial
- XGBoost1 Item
- ML Model Deployment with AWS11 Items
- 3.1Exploring Methods for Deploying Models in Production
- 3.2Deploying in Amazon SageMaker [Hands-On]
- 3.3Comparing Types of Inference: Serverless vs Real-Time vs Asynchronous
- 3.4Using Deployment Endpoints in Amazon SageMaker [Hands-On]
- 3.5Using Infrastructure as Code (IaC): CloudFormation
- 3.6Using Infrastructure as Code (IaC): Cloud Development Kit (CDK)
- 3.7Understanding Docker Containers and Containerized Applications
- 3.8Exploring Elastic Container Registry (ECR), Elastic Container Service (ECS) and Fargate
- 3.9Building Containers Using Amazon ECR [Hands-On]
- 3.10Building Containers Using Amazon ECS [Hands-On]
- 3.11Building Containers Using Amazon EKS [Hands-On]
- Resources1 Item
- Practice Lab1 Item
- Claim Your Certificate1 Item
AI and Machine Learning Deployment to Production Practice Lab
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