What Is Automatic Machine Learning (AutoML)?

Instead of relying on humans to construct and fine-tune machine learning models, automated systems may now do it using a technique known as automatic machine learning (or #AutoML for short). It authorizes computers to learn from data without being explicitly programmed. Before we go into the nuts and bolts of it, let's have a little fun with the explanation. Now, put yourself in the shoes of a baker attempting to create the best possible batch of cookies. You've got the basic ingredients down pat (flour, sugar, eggs, and butter), but you need to figure out how much to use. The next step is to try out a few different recipes and change the amounts of each ingredient until you get the flavor you want. AutoML performs a similar function. Instead of making cookies, it builds machine-learning models and chooses the right algorithms and hyperparameter values. AutoML works by trying out different combinations of algorithms and hyperparameters over and over again, similar to how a baker might try out other ingredients to find the best cookie recipe. Let's dive into the nitty-gritty details now. The term "#HyperparameterOptimization" describes one of the AutoML methods that has gained popularity. This entails training many models with varying settings for various hyperparameters (such as learning rate and regularisation strength). Ultimately, the model with the highest performance is chosen (based on some criterion such as accuracy or precision). Machine learning can also be used to find the best way to design a neural network for a certain task. This is called #NeuralArchitectureSearch and is one of the ways that AutoML works. The number of levels, the number of units in each layer, and the sorts of connections between layers are all factors that can be selected. Google's AutoML, H2O's DriverlessAI, and DataRobot are just a few tools and frameworks that make it simpler to put AutoML into practice. To sum up, automatic machine learning (AutoML) lets machines build and improve machine learning models without help from humans. This is done through hyperparameter optimization and neural architecture search. Using tools like Google AutoML and H2O DriverlessAI, you can automate the model selection process to save time and money.