Automated machine learning

AI is powered by machine learning (ML). The way things stand right now, you most probably have to find someone to build a machine learning model for you. In an ideal world, the person who builds the machine learning model should know your business well enough and should have advanced programming and mathematical skills. However, there aren’t enough data scientists! How then will you become an AI-driven enterprise or have a competitive edge over your peers?

The key is to identify employees who know your business best and empower them with the right AI tools. A powerful AI tool like Brainalyzed Insight knows which algorithms to select and how to train the model. It knows precisely how to make those decisions so that your team of business experts can focus on solving the problem at hand.

What is automated machine learning?

Automated machine learning, also known as automated ML is the process of automating time-consuming and repetitive tasks within the development cycle of an ML model. This leaves room for your team to build ML models at scale without compromising on quality.

Traditional ML model development is resource and time-intensive and requires significant domain knowledge on the part of your tech team. However, with automated ML, you can accelerate the time taken to build production-ready ML models with greater ease and efficiency. Does that mean, everyone can automate AI and leverage it for their business purposes without having to worry about hiring data scientists or analysts? The answer is not that simple.

While the real purpose of technology is to automate tasks and make life as easy as possible for humans, you need to understand that it is not possible to automate everything. This means you will need human minds working in conjunction with artificial intelligence to build state of the art AI models for your business.

Finding and retaining data scientists is one of the biggest challenges in AI adoption. However, with the help of automated machine learning, you can empower your business teams to build AI models with ease. This also allows data scientists to focus on more productive and complex tasks.

Creating a class of citizen data scientists

Automated machine learning is creating a new class of citizen data scientists. Gartner defines a citizen data scientist as someone who augments data discovery and simplifies data science. Though their primary job function is outside the field of statistics and analytics, they create AI models that use advanced predictive capabilities. Think of them as power users who are capable of performing moderately sophisticated analytical tasks that would otherwise require a lot more expertise. They play a complementary role to data scientists.

Key steps to automated machine learning

Preparing data

Each machine learning algorithm works differently and has different data requirements. The automated machine learning platform can transform raw data into a structured format based on the requirements of the algorithm so that the algorithm’s performance is optimal.

Feature engineering

It is the process of modifying data so that the algorithms can work better. The machine learning platform should be able to engineer new features from various types of feature such as text, numerals, etc. It should be able to generate only those features that are of value depending on the characteristics of the data. We at Brainalyzed believe that feature engineering is expensive and time-consuming and doesn’t quite add sufficient value to the overall automated ML process.

Selecting the right algorithm

There are plenty of algorithms available today. However, it is impractical to explore every algorithm on the data you have. An automated ML platform will select and run on only those algorithms that are appropriate or suitable for your data.

Training ML models

Training an ML model on your data is a standard step in the ML process. A good AI platform knows what features to include in the training and what to weed out. By using a method called hyperparameter tuning, the platform can tune the most important hyperparameters for each algorithm as part of the training.

Easy-to-understand insights

Machine learning has proven its capability in various ways. It can make accurate predictions but at the cost of complexity. It isn’t just enough if an ML model is accurate and quick. It should also be able to translate the predictions in a human-friendly way. The outcomes and predictions should also be trustworthy. A good ML platform can explain the predictions that it can justify and are also easy for humans to interpret.

Ease of deployment

It is one thing to build a great predictive model but if you do not have the necessary infrastructure to implement the model in the production setting, it is a waste of time for your analysts. You need to opt for an ML platform that lets you build models that are ready for deployment.

Monitoring and management

Business requirements are constantly changing. The AI models you build should be able to keep up with the changing business needs. You need to keep them up to date with the newest trends as well. With an automated ML platform, you will be able to compare predictions with actual results and update the AI model with the latest information. It will also be able to identify when a model’s performance is deteriorating and notify you.

Closing thoughts

The basics of data science are fast becoming common knowledge. While not everyone is familiar with advanced statistics, crucial concepts such as variance and distributions are common knowledge. This helps in informing business decisions. Similar to how MS Excel democratized data storage and its manipulation, Automated machine learning is democratizing data science for companies and business teams.