SageMaker AutoPilot
- automates the process of building, tuning, and deploying machine learning models based on a tabular dataset (CSV or Parquet).
- automatically explores different solutions to find the best model.
SageMaker GroundTruth
- a data labeling service that lets you use workforce (human annotators) through your own private annotators, Amazon Mechanical Turk, or third-party services.
SageMaker Data Wrangler
a visual data preparation and cleaning tool that allows data scientists and engineers to easily clean and prepare data for machine learning.
SageMaker Neo
allows you to optimize machine learning models for deployment on edge devices to run faster with no loss in accuracy.
SageMaker Automatic Model Tuning
- automates the process of hyperparameter tuning based on the algorithm and hyperparameter ranges you specify.
- This can result in saving a significant amount of time for data scientists and engineers.
Amazon SageMaker Debugger
- provides real-time insights into the training process of machine learning models, enabling rapid iteration.
- It allows you to monitor and debug training issues, optimize model performance, and improve accuracy by analyzing various model-related metrics, such as weights, gradients, and biases.
Managed Spot Training
– allows data scientists and engineers to save up to 90% on the cost of training machine learning models by using spare compute capacity.
SageMaker Studio
A web-based IDE for machine learning. It provides tools for the entire ML lifecycle, including data wrangling, model training, and deployment, all in one unified interface. Helps data scientists and developers quickly build and train models and streamline ML workflows.
SageMaker Notebooks
- A fully managed, scalable Jupyter notebook for quick data exploration, model building, and training. It helps you start working on ML models immediately without managing infrastructure.
SageMaker Distributed Data Parallelism (SMDDP)
- A feature that enables efficient distributed training of deep learning models by automatically parallelizing data across multiple GPUs and instances.
- Speeds up the training of large models on massive datasets, improving scalability and reducing training time.
- It supports frameworks like TensorFlow and PyTorch, making it ideal for large-scale deep-learning tasks that require intensive computational resources.
SageMaker Pipelines
A fully managed CI/CD service for automating the end-to-end machine learning workflow, including data preprocessing, model training, and deployment. It helps automate and streamline the ML lifecycle, ensuring consistency and efficiency.
SageMaker Model Monitor
- Monitors models in production to detect issues such as data drift or model performance degradation.
- Ensures that models continue to perform accurately after deployment.
SageMaker Model Registry
A centralized repository for managing ML models, including tracking versions and promoting models for deployment. Ensures proper model version control and governance across teams.
SageMaker Edge Manager
offers model management for edge devices, enabling you to optimize, secure, monitor, and manage machine learning models on various edge device fleets, including smart cameras, robots, PCs, and mobile devices.
SageMaker Feature Store
a fully managed repository designed to store, share, and manage features for machine learning models. It ensures high-quality, standardized features are available for both training and real-time inference, helping teams keep their feature data synchronized and consistent.
SageMaker JumpStart
provides pre-trained foundation models and ready-to-use solutions for common machine learning tasks like text summarization, image generation, and object detection, enabling users to deploy and experiment without deep expertise quickly.
What are the two input modes for transferring training data?
File mode and Pipe mode
File mode
- Downloads data into the SageMaker instance volume before model training commences
- Slower than pipe mode
- Used for incremental training
Pipe mode
- Directly stream data from Amazon S3 into the training algorithm container.
- There’s no need to procure large volumes to store large datasets.
- Provides shorter startup and training times.
- Higher I/O throughputs
- Faster than File mode.
- You MUST use protobuf RecordIO as your training data format before you can take advantage of the Pipe mode.
What should you do if training via File mode is too slow?
- Convert training data into a protobuf RecordIO format to make use of Pipe mode.
- Use Amazon FSx for Lustre to accelerate File mode training jobs.
What are the two ways to deploy a model of inference?
- Amazon SageMaker Hosting Services
- Amazon SageMaker Batch Transform
Amazon SageMaker Batch Transform
- Batch inference
- Doesn’t need a persistent endpoint
Amazon SageMaker Hosting Services
- Provides a persistent HTTPS endpoint for getting predictions one at a time.
- Suited for web applications that need sub-second latency response.
SageMaker Content-Based Filtering
- Only applicable to recommendation systems
- If you watch scifi, the content-based filtering will recommend scifi
- Not a good solution for image content moderation (use Rekognition for that)
