Using Python for AI applications

Python is one of the most popular programming languages. Thanks to its popularity with the data science and ML community, Python is also one of the fastest-growing programming languages. There is a large number of add-on libraries from its developers and the open source community. These libraries enable Python developers to do almost anything, from data analytics to deep neural networks, from simple scripting to web application development.

For AI and ML, Python is the de facto language. There's the popular scikit-learn library that gives developers access to many useful ML algorithms. There are also many libraries for deep neural networks, such as MXNet and TensorFlow.

We will be using Python for every hands-on projects throughout this book:

• In the first half of this book, we will create intelligent-enabled solutions using AWS AI services. For these projects, we will use Python to create the backend components, APIs, and web applications that will bring our intelligent creations to life. AWS offers a Python SDK called Boto. With Boto, we can interact with all of the AWS services from our applications, including the managed AI capabilities.
• In the second half of this book, we will be training custom ML models using AWS ML services. For these projects, we will use Python to process data, train ML models, and deploy intelligent capabilities. In addition to the Boto SDK, we will also use AWS libraries for SageMaker, Elastic MapReduce (EMR), and many more.