What is AutoML?

AutoML, short for Automated Machine Learning, is all about making machine learning (ML) easier and faster to use. Instead of manually doing all the complex steps that go into building and tuning an ML model, AutoML systems support you in most of these steps, saving time and improving results.

Full AutoML systems make machine learning accessible. As data scientists, you’ll be able to quickly create and test models without needing to do all the work by hand. This is especially helpful when you’re working with large datasets or when you’re under time pressure.

Hyperparameters are settings that control how an ML model learns. Just like we have different settings on a washing machine (temperature, spin speed, etc.), ML models have hyperparameters that affect their performance. But tuning these by hand is tricky and time-consuming, and it’s easy to make mistakes. In fact, the success of your entire machine learning training can depend on hyperparameters like the learning rate. Hyperparameter optimization is a process that automatically and efficiently searches for the best combination of settings for our model, making it perform better without a lot of trial and error.

Imagine trying to find the best recipe by changing ingredients one by one without any guidance—it can take forever! Hyperparameter optimization speeds up this process by finding good settings automatically, giving us a better model with less effort.

If you’ve heard of deep learning and neural networks, you know that building a powerful model can be complex, especially when there are lots of layers, operators and connections to design. Neural Architecture Search (NAS) partially automates the design of these neural networks, allowing the system to find the best structure for a given task. It’s like AutoML designing the “blueprint” of ours model.

Creating a neural network by hand is challenging, even for experts. NAS allows us to create complex, high-performing neural networks without needing deep technical knowledge, which is a huge benefit if we want to use deep learning in our work.

In a typical machine learning project, there are many steps: data cleaning, feature selection, model training, and so on. A data science pipeline is a sequence of these steps, and each step impacts the model’s performance. Pipeline optimization means finding the best combination and order of these steps automatically, so we don’t have to spend hours adjusting them manually.

Optimizing every step in a data science pipeline by hand is difficult, especially when working with big data or when each step has many possible settings. Pipeline optimization can improve your model’s accuracy and save time by finding the best path for us.

Meta-learning, or sometimes called “learning to learn,” is a method where the AutoML system uses knowledge from past machine learning tasks to make better predictions on new tasks. For example, if a system has worked on several projects with similar data, it “learns” from those experiences and uses that knowledge to make new projects easier and faster to build.

Think of meta-learning as giving the AutoML system a memory. When it remembers which techniques worked well before, it can suggest the best methods faster. This means that we can get helpful recommendations right away without needing a lot of experience. Furthermore, AutoML with meta-learning

Reinforcement learning (RL) is a simple yet powerful paradigm for training intelligent agents to perform a given task. To do so, RL agents interact with the world in which they exist. Guided by a reward signal, RL agents learn in a trial-and-error fashion: RL agents follow a policy, observe the result, and, depending on this outcome, update their policy to get better at a given task. AutoRL enhances this process by automating the selection of algorithms, architectures, hyperparameters, and task specifications.

Existing RL algorithms are often brittle, sensitive to minor implementation details, and highly dependent on specific experimental setups, limiting their adoption in real-world tasks. AutoML, however, offers a range of approaches to address these challenges in RL. Conversely, examining various facets of RL presents new avenues for innovation within AutoML research, creating a mutually beneficial area of exploration for both fields.