[MLS-C01] [IMPL and OPs] Build solutions

Toolset

SageMaker makes it simple to develop high quality models by enabling quick deployment at scale
SageMaker provides the tools needed for machine learning in a single toolset that allows you to get models to production faster with much less effort and at lower cost than with the traditional tool suite
- SageMaker Studio
- SageMaker Autopilot
- SageMaker Experiments
- SageMaker Debugger
- SageMaker Model Monitor

Single, web-based visual interface where you can perform all your machine learning development steps

Build, train, and deploy models
Upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production

Build, train, and tune models with complete visibility and control

Inspects input data, applies feature engineering, picks optimum set of algorithms, trains and tunes multiple models, tracks model’s performance, and ranks your models based on their performance
Gives you the best model for your problem at hand while saving time
Can be used by developers without extensive machine learning experience

Organize your artifacts, track metrics, and evaluate training runs using SageMaker Experiments

Manage your model iterations by capturing the input parameters, configurations, and results, and saving them as experiments

Analyze, identify, and alert problems for machine learning

Track real-time metrics when training such as validation, confusion matrices, and learning gradients to increase mode accuracy
Generate warnings and corrective action advice when you experience training issues

Detect and remediate concept drift where the patterns used to train the model have changed over time

Identifies concept drift in deployed models and gives detailed notifications that help identify the source of the drift

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