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 […]

Exam Readiness Drill – Chapter Review Questions Apart from a solid understanding of key concepts, being able to think quickly under time pressure is a skill that will help you ace your certification exam. That is why working on these skills early on in your learning journey is key. Chapter review questions are designed to […]

Sales Forecasting Model with Amazon Forecast Let’s dive into a hands-on example of using Amazon Forecast to build a sales forecasting model. In this example, you’ll predict future sales based on historical data. Set up your dataset: Prepare a dataset containing historical sales data, ensuring it includes relevant timestamps and corresponding sales figures. Create a […]

Getting hands-on with Amazon Lex Let’s get started: Figure 8.25 – The Create dialog of Amazon Lex Some sample utterances can be seen in Figure 8.26. In this example, movie_type is my variable: Figure 8.26 – The Sample utterances section Figure 8.27 – The Response section of Amazon Lex Figure 8.28 – The Response section […]

Important note The most scalable and cost-effective way to generate S3 PJUT events for asynchronously invocating downstream AI workflows via Lambda is to generate an AWS pre-signed URL, and then provide it to your mobile or web application users. Many users can be served at the same time via this approach, and it may increase […]

Getting hands-on with Amazon Textract In this section, you will use the Amazon Textract API to read an image file from our S3 bucket and print the FORM details on Cloudwatch. The same can be stored in S3 in your desired format for further use or can be stored in DynamoDB as a key-value pair. […]