What is TensorFlow?

- [Presenter] Let's break down what's possible with TensorFlow. Now, we all know that data is key to making your machine learning models work well, and TensorFlow has some brilliant tools to help you out with that. You've got access to standard data sets to kickstart your training and validation, which is super handy and then TensorFlow also has these data pipelines that can handle massive amounts of data. Now, if you need to pre-process your data, they've got layers for that too, so you can transform your inputs however you want. Okay, so you're probably thinking, building and training machine learning models sounds like a lot of work, and you're right, but TensorFlow's core framework has got you covered with an entire ecosystem designed to streamline that process. So we're talking model construction, training, and export, all made easier with this framework. But that's not even the best part, TensorFlow supports distributed training, which means you can spread the workload across multiple machines and get your models trained faster. And if you're a fan of Keras, you'll be happy to know it works really well with TensorFlow. TensorFlow also comes with tools like Model Analysis and TensorBoard, which helps you to keep track of your model's development and improvements throughout its lifecycle. So it's like having a personal assistant for your machine learning training. The best part is you don't even have to train your models from scratch. TensorFlow Hub and the Model Garden offers pre-trained models from Google and the community. So these high level components are like building blocks and you can use them to fine tune models for your specific needs or customize them to tackle entirely new tasks, it's like having a head start. Okay, so you're probably wondering where you can actually use these TensorFlow models you've been working on, right? Well, let me tell you, the possibilities are endless, servers, edge devices, browsers, mobile apps, you name it, TensorFlow can handle it. Now, if you need to really scale up for production, there's TensorFlow Serving, which can tap into some seriously powerful hardware like Google's own TPUs or Tensor Processing Units. Now, if you need to analyze data locally, maybe to reduce latency or keep things secure, TensorFlow Light lets you run models on mobile devices, edge computing rigs, and even tiny microcontrollers. And get this, you can even run machine learning models straight from your web browser with TensorFlow.js. Now, imagine you're running a business that relies heavily on machine learning models. As time goes by, your data and the requirements start to shift and suddenly your once brilliant models aren't performing as well as they used to. Now, this is common knowledge if you've been working in ML for a while, and this is what ML Ops is all about. TFX is a set of tools from TensorFlow that helps you automate the entire ML Ops process. You can track your model training, monitor performance, and even retrain your models when needed.