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 […]

Getting hands-on with Amazon SageMaker notebook instances The very first step, in this section, is to create a Jupyter Notebook, and this requires a notebook instance. You can start by creating a notebook instance, as follows: Figure 9.3 – Amazon SageMaker role creation sh-4.2$ cd ~/SageMaker/ sh-4.2$ git clone https://github.com/PacktPublishing/AWS-Certified-Machine-Learning-Specialty-MLS-C01-Certification-Guide-Second-Edition.git Figure 9.4 – Jupyter Notebook […]

Training Data Location and Formats As you embark on the journey of setting up your AWS SageMaker training job, understanding the diverse data storage and reading options is crucial. To ensure a seamless training experience, delve into the supported options and their benefits. First you will look at the supported data storage options: Here are […]

As you can see in Figure 9.2, Training offers Algorithms, Training jobs, and Hyperparameter tuning jobs. Let’s understand their usage: Inference has many offerings and is evolving every day: You have got an overview of Amazon SageMaker. Now, put your knowledge to work in the next section. Important note The Amazon SageMaker console keeps changing. […]

Amazon SageMaker Modeling In the previous chapter, you learned several methods of model optimization and evaluation techniques. You also learned various ways of storing data, processing data, and applying different statistical approaches to data. So, how can you now build a pipeline for this? Well, you can read data, process data, and build machine learning […]