SageMaker Training Compiler If you’ve reached this section, you are about to delve into the world of SageMaker Training Compiler (SMTC), a game-changing tool designed to supercharge the training of your ML models on SageMaker by optimizing intricate training scripts. Picture this: faster training, swifter model development, and an open door to experimentation. That’s the […]

SageMaker Debugger In this section, you will learn about Amazon SageMaker Debugger, unraveling the intricacies of monitoring, profiling, and debugging ML model training: In a nutshell, Amazon SageMaker Debugger emerges as a holistic toolkit, empowering you to monitor, profile, and debug your ML models with finesse. It’s not just a tool; it’s your ally in […]

SageMaker Model Monitor In the ever-evolving realm of ML, ensuring the reliability and robustness of models in real-world production settings is paramount. In this section, you will delve into the profound significance, practical applications, and potent features of Amazon SageMaker Model Monitor—an instrumental component tailored to tackle the challenge of model drift in live production […]

SageMaker Autopilot ML model development has historically been a daunting task, demanding considerable expertise and time. Amazon SageMaker Autopilot emerges as a game-changer, simplifying this intricate process and transforming it into a streamlined experience. Amazon SageMaker Autopilot presents a rich array of features to facilitate the development of ML models: Amazon SageMaker Autopilot heralds a […]

Securing SageMaker notebooks If you are reading this section of the chapter, then you have already learned how to use notebook instances, which type of training instances should be chosen, and how to configure and use endpoints. Now, let’s learn about securing those instances. The following aspects will help to secure the instances: In this […]

Taking care of Scalability Configurations To kickstart auto scaling for your model, you can take advantage of the SageMaker console, AWS Command Line Interface (AWS CLI), or an AWS SDK through the Application Auto Scaling API. For those inclined towards the CLI or API, the process involves registering the model as a scalable target, defining […]

Important note To remember this easily, you can think of t for Tiny, m for Medium, c for Compute, and p and g for GPU. The CPU-related family instance types are t, m, r, and c. The GPU-related family instance types are p and g. Choosing the right instance type for a training job There […]

Important note Please note that you are using other variables in this configuration file, bucket and prefix, which should be replaced by your bucket name and prefix key (if needed), respectively. You are also referring to s3_input_train and s3_input_validation, which are two variables that point to the train and validation datasets in S3. Once you have set […]

Model tuning In Chapter 7, Evaluating and Optimizing Models, you learned many important concepts about model tuning. Let’s now explore this topic from a practical perspective. In order to tune a model on SageMaker, you have to call create_hyper_parameter_tuning_job and pass the following main parameters: In SageMaker, the main metric that you want to use […]

Getting hands-on with Amazon SageMaker’s training and inference instances In this section, you will learn about training a model and hosting the model to generate its predicted results. Let’s dive in by using the notebook instance from the previous example: Figure 9.7 – The InService instance Figure 9.8 – The SageMaker fit API call Figure […]