Automated Machine Learning (AutoML) offers a golden solution for companies that want to get more out of their data, but do not have the experts to invest hours in it. AutoML is the way to bridge this gap. The technique allows you to build the models you need for data analysis within your company much faster. In this article, we look at the advantages and the application possibilities of AutoML. But before that, we need to take a look at what AutoML exactly is and how it relates to Machine Learning.
What is Machine Learning?
Machine Learning (ML) is a form of Artificial Intelligence (AI) and focuses on techniques that allow computers to use input data and patterns to learn and draw conclusions. Although the computer does a lot of work, Machine Learning requires a large investment of time from experts. While in practice, the work these people have to do is often not that exciting. Most of their time is spent selecting, structuring and preparing data. It is a labour-intensive process that must be repeated again and again in order to arrive at the models that are of value to your business.
Automated Machine Learning versus Machine Learning
Opposite Machine Learning today is Automated Machine Learning. This technique makes it possible to outsource all the routine and time-consuming work mentioned above to machines. As a result, you get the same result in much less time. Moreover, with AutoML, it is also possible for people who are not experts in Machine Learning to manage the process. With the scarcity in the labour market due to the increasing demand for data scientists and ML experts, it is no unnecessary luxury to work with technology for which you are not completely dependent on this group of underrepresented professionals.
Benefits of Automated Machine Learning
You almost forget it in these times of strong focus on technological innovation, but in the end, technology is only there to support your business. In other words: your data analysis is not your core business. Your data analysis must only your core business. As an entrepreneur, it is therefore beneficial to choose technology that works as efficiently as possible.
Then your analysts can put their cognitive energy back into thinking about business problems instead of doing endless repetitive work before the technology can do its job. Taken together, Automated Machine Learning brings the following benefits to your business.
The applications of AutoML
The applications of AutoML are endless! But we would like to give you an idea of what is possible.
1. Forecasting on the basis of figures
As an entrepreneur, you naturally want to know what you are working towards. ML engineers and data scientists use time-series forecasting to predict future events. For this they analyse data and see how certain values develop through time. It is a complex process that normally takes a lot of time and work. In particular, distilling the right signals and determining how past events will affect the future is complicated.
Automating ML models
ML models normally require constant manual rebuilding and updating. AutoML on the other hand automates the whole process of forecasting. It also includes the discovery of future predictive signals, values and parameters. In other words: AutoML always adapts the model to the new situation. This means you have much less to worry about. Classifying, selecting algorithms, testing models, tuning models: AutoML can do it all.
2. Forecasting on the basis of words
When you think of data, you immediately think of numbers? That is not surprising: many people and certainly many entrepreneurs are fixated on numbers. Yet our society is ultimately also (and perhaps primarily!) built on language. Almost all communication takes place in language. And not only the content but also the tone and choice of words contain invaluable information. However, language on a large scale is perhaps even more difficult for people to qualify than large data sets with numbers.
Natural Language Processing
Just as ML can distil meaning from large data sets consisting of numbers, machines can also read and understand language. This is called Natural Language Processing. This is how you build models that scan large collections of documents for important information and even distil a sentiment from it. Just like people, but on a much larger scale and without losing themselves in their own subjective connotations. Imagine the possibilities this offers to gain insight into the impact of a certain news event or the launch of a certain product on the reputation of your company.
AI models are determined by algorithms, data transformations and parameters, all three of which must be coordinated for the best performance. The selection and interaction between algorithm, data processing and parameterisation is carried out independently by the Trendskout AutoML. This speeds up the entire process: from conceptualisation to delivery of a high-performance application for AI and Deep Learning. For more information, please feel free to contact with us!