- A fully managed service that allows data scientists and developers to easily build, train, and deploy machine learning models at scale.
- Provides built-in algorithms that you can immediately use for model training.
- Also supports custom algorithms through docker containers.
- One-click model deployment.
- It refers to a set of variables that controls how a model is trained.
- You can think of them as “volume knobs” that you can tune to acquire your model’s objective.
- Automatic Model Tuning
- Finds the best version of a model by automating the training job within the limits of the hyperparameters that you specified.
- The process where you create a machine learning model.
- The process of using the trained model to make predictions.
- Local Mode
- Allows you to create and deploy estimators to your local machine for testing.
- You must install the Amazon SageMaker Python SDK on your local environment to use local mode.
Common Training Data Formats For Built-in Algorithms
- Protobuf RecordIO
Input modes for transferring training data
- 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.
Two methods of deploying a model for inference
- 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.
- Amazon SageMaker Batch Transform
- Doesn’t need a persistent endpoint
- Get inferences for an entire dataset
- 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.
- You can publish SageMaker instance metrics to the CloudWatch dashboard to gain a unified view of its CPU utilization, memory utilization, and latency.
- You can also send training metrics to the CloudWatch dashboard to monitor model performance in real-time.
- Amazon CloudTrail helps you detect unauthorized SageMaker API calls.
- The building, training, and deploying of ML models are billed by the second, with no minimum fees and no upfront commitments.