Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. It removes the complexity that gets in the way of successfully implementing machine learning across use cases and industries—from running models for real-time fraud detection, to virtually analyzing biological impacts of potential drugs, to predicting stolen-base success in baseball.
Amazon SageMaker Studio: Experience the first fully integrated development environment (IDE) for machine learning with Amazon SageMaker Studio, where you can perform all ML development steps. You can quickly upload data, create and share new notebooks, train and tune ML models, move back and forth between steps to adjust experiments, debug and compare results, and deploy and monitor ML models;
Amazon SageMaker Autopilot: Automatically build, train, and tune models with full visibility and control, using Amazon SageMaker Autopilot.
AWS pre-trained AI Services provide ready-made intelligence for your applications and workflows. AI Services easily integrate with your applications to address common use cases such as personalized recommendations, modernizing your contact center, improving safety and security, and increasing customer engagement. AI Services on AWS don't require machine learning experience.
To be honest with you, in this open space, I am unable to share my techniques, models etc. Kindly initiate a chat and I will share the details;