AWS-Practical-Data-Science

-Develop and scaling data science projects into the cloud using Amazon SageMaker.
-This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.

Applied Learning Project Specialization focused the following skills, you will be ready to:

• Ingest, register, and explore datasets

• Detect statistical bias in a dataset

• Automatically train and select models with AutoML

• Create machine learning features from raw data

• Save and manage features in a feature store

• Train and evaluate models using built-in algorithms and custom BERT models

• Debug, profile, and compare models to improve performance

• Build and run a complete ML pipeline end-to-end

• Optimize model performance using hyperparameter tuning

• Deploy and monitor models

• Perform data labeling at scale

• Build a human-in-the-loop pipeline to improve model performance

• Reduce cost and improve performance of data products

Analyze Datasets and Train ML Models using AutoML

• Prepare data, detect statistical data biases, and perform feature engineering at scale to train models with pre-built algorithms.

Build, Train, and Deploy ML Pipelines using BERT

• Store and manage machine learning features using a feature store • Debug, profile, tune and evaluate models while tracking data lineage and model artifacts

Optimize ML Models and Deploy Human-in-the-Loop Pipelines

• Human-in-the-Loop Pipelines • Distributed Model Training and Hyperparameter Tuning • Cost Savings and Performance Improvements • A/B Testing and Model Deployment • Data Labeling at Scale