Free ebookTensorFlow for Beginners: Building and Serving Your First Models

Course Description

TensorFlow for Beginners: Building and Serving Your First Models is a practical ebook course for anyone entering Artificial Intelligence within Information Technology who wants to move beyond demos and start building usable machine learning solutions. You will learn how to create, train, export, and serve your first TensorFlow model with clear explanations and hands on workflows you can apply to real projects.

You begin with TensorFlow and Keras essentials and quickly get comfortable with tensors, including shapes, dtypes, and core operations that power everyday model code. As you progress, you will build reliable input pipelines with tf.data.Dataset so your training process can scale from small experiments to real datasets while staying clean and repeatable.

Model design is taught through Keras layers, correct shape management, and both Sequential and Functional API patterns, helping you choose the right structure for the problem. Training is covered in a practical way using model.fit with losses, optimizers, and metrics, then extended with custom training using GradientTape when you need more control for advanced logic, debugging, or research style experimentation.

To make your work reusable, you will learn best practices for saving and loading TensorFlow models as portable artifacts, so your results can move from development to production. You will also explore basic model serving patterns, from Python inference scripts to HTTP endpoints, connecting your Artificial Intelligence model to real applications. Along the way, the course emphasizes clean project structure, showing how to evolve from a notebook prototype into a maintainable codebase that is easier to test, update, and deploy.

The course concludes with an end to end capstone where you train, export, and serve a TensorFlow model, tying together the complete workflow you need for practical machine learning delivery. Start learning today and build your first deployable TensorFlow project with confidence.