Sagemaker

1. End-to-End ML Workflow: Complete ML workflow from data preparation and model training to deployment and monitoring.

2. Managed Infrastructure: Fully managed infrastructure for training and hosting ML models, with automatic scaling.

3. Built-in Algorithms: Access to a wide range of built-in ML algorithms for common tasks like regression, classification, and clustering.

4. Support for Custom Models: Flexibility to bring your own algorithms and frameworks like TensorFlow, PyTorch, and MXNet.

Before learning Amazon SageMaker, it's beneficial to have the following skills:

1. Basic Machine Learning: Understanding of fundamental machine learning concepts such as supervised learning, unsupervised learning, and model evaluation metrics.

2. Programming Skills: Proficiency in programming languages commonly used in machine learning such as Python. Knowledge of libraries like NumPy, Pandas, and Scikit-learn is also helpful.

3. Data Manipulation: Ability to manipulate and preprocess data using tools like Pandas and libraries for data visualization like Matplotlib or Seaborn.

4. AWS Fundamentals: Familiarity with Amazon Web Services (AWS) and its core services such as S3, IAM, and EC2.

By learning Amazon SageMaker, you gain the following skills:

1. End-to-End Machine Learning Workflow: Ability to manage the entire machine learning lifecycle, from data preparation and model training to deployment and monitoring.

2. Model Development: Proficiency in developing machine learning models using built-in algorithms, custom algorithms, or pre-trained models.

3. Hyperparameter Optimization (HPO): Knowledge of hyperparameter tuning techniques to optimize model performance.

4. Model Deployment: Skill in deploying trained models to production environments using SageMaker's hosting services.