Keras is a popular high-level deep learning framework that provides a user-friendly interface to build and train neural networks. One of the key components of Keras is the Functional API, which allows you to define complex models with more flexibility than the Sequential model. In this blog, we'll explore the Keras Functional API and learn how to use it to build complex deep learning models. Let's take a look at the topics we'll cover. First, we'll briefly discuss what the Keras functional API is.

The Keras Functional API provides a way to build flexible and complex neural networks in TensorFlow. The Functional API is used to design networks that are not linear. We used the Sequential API in the CNN tutorial to build an image classification model with Keras and TensorFlow. The Sequential API involves stacking layers. One layer is followed by another layer until the final dense layer.

An end-to-end Machine Learning solution is an important way to bring AI to production and make it available for mass consumption and usage. But today, most AI practitioners simply do the pre-processing, training, evaluation and tuning stages and leave the remaining part to DevOps engineers. As such, a new field of development named MLOps has come into the mainstream. The focus has shifted from simply training and evaluation to also bringing and integrating it to production pipelines. On an individual level as well, knowing how to bring your model to the public is an important tool to have in an AI practitioner's skill-set.

Getting started with deep learning has become very simple and convenient, all thanks to wonderful duo of keras and tensorflow. You just need to do some imports, define some layers and bingo, you have your deep learning architecture ready to be trained and eventually give some amazing results. Keras has made such an amazing abstraction that even a total stranger to the topic as well can start training their own deep learning models. However if you are calling yourself a Data Scientist/Machine Learning Engineer then having some basic understanding of what's happening under the hood is a must, I am not saying you need to exactly know the hundreds of lines of code behind it but at least have a some understanding what those lines of code are doing. Without wasting anytime lets dive into some of the most commonly used keras components and try to understand them piece by piece, this will involve a basic understanding of Object Oriented Programming but don't worry I will try to keep it as simple as possible.

TensorFlow has recently released its new update, TensorFlow 2.4.0-rc4. TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy that is now a stable API and tracing multiple workers using the sampling mode API. This strategy enables synchronous distributed training across multiple workers, each with potentially multiple GPUs. Some of the significant improvements involve handling peer failure and many bug fixes that can be found on Multi-worker training with Keras. Major refactoring of the internals of Keras Functional API has been completed.