Frequently Asked Questions

Machine learning is a subset of artificial intelligence that deals with the design and development of algorithms that can learn from and make predictions on data.

There are many benefits of machine learning, including: the ability to make predictions on data sets too large for humans to process, the ability to find patterns in data that humans would not be able to find, and the ability to make decisions in real-time.

Machine learning algorithms works by building models from data that can then be used to make predictions on new data. The process of building the models is known as training, and the process of using the models to make predictions is known as inference.

Suppose you have a small franchise specializing in selling bikes. Your growth is not in line with your expectations so you begin to examine specific performances of each company. Firstly, for each company, you gather numerical data which you believe may affect how many bikes can be sold on a given day such as: details about the date, details about the weather and the number of bikes the company sold that day. You then think, what method can I use to predict how many bikes that each company will sell in the future, based on this data? Well, this is where machine learning comes in. Cutting-edge model algorithms can be trained on your existing data to formulate trends to predict future bike sales. This is all performed via rigorous testing and thorough self-evaluation at frequent intervals of the training to tune a deeper understanding of the dataset, all before the model arrives to its poised predictions. To boost reliability of these algorithms even more, we can add further preprocessing and post-processing to be applied to the algorithm being used, making it unique to your dataset. Interested? We have a demo showcasing this.

Need to process content-heavy data spread across multiple pages automatically? Manually gathered some historical data and want to make predictions based on it? Want to find relationships and trends amongst pre-existing data. These are just few of many scenarios that can be solved by machine learning. Want some more examples? Have a look at some of our past work!

The answer is: it depends. It used to be that you needed a lot of data to train useful models. However, with the advent of modern ML techniques, such as data augmentation and large pretrained models (e.g., GPT-3), it is now possible to create ML models with only a little amount of data. Also, you may have more data than you think!

Do you think you don't have any data we could work on? Don't worry, we can still do something for you. There are many datasets and online resources we could leverage. Most importantly, you might not realise that you already have useful data. It is just to be found in unexpected places such as website traffic, sensors, paperworks, etc.

We can leverage data from all sorts of sources to produce value for your company! Social media posts, customer service conversations, team and resource schedules, biometric sensors, satellite imagery, social networks, product reviews, knowledge graphs... the possibilities are endless. Come with your data and we'll be happy to show you how machine learning can help you get the most out of it!

Do you have data that you're not quite sure how to use? We can help you with that. We're experts at acquiring and integrating new data sources, so you can get the most out of your data. Whether it's in the form of Excel files, databases, websites, or simply a disorganized collection of files, we can turn it into usable data that will help your business grow. Contact us today to get started.

Machine learning has numerous advantages, one of which is personalization. By altering machine learning models and their data, we can better suit your needs. For example, by only selecting the relevant information, we can make faster and better predictions. We improve data quality by resolving missing or incorrect data. We search over many models and their parameters to find the best ones. In the process, we keep track of the earlier models' performances, so that we can visualize the progress using tools like Power BI. To find out more, check out our typical process now!

Of course! In collaboration with the model being trained in preparation for predicting, we can use external tools to provide a detailed visual representation of the performance of this model. As an example, the performance of a model can first be stored in a SQL database. Then Power BI, with the help of in-built Python scripting, extracts the data from this database and uses the data to generate graphs based on wanted performance factors. This can range from error analysis of the model to using correlation plots to show relationships between features. Want a live example of this in action? Check out our work on bike rentals.

Well, this depends on the type of data you have. You have historical data that you wish to make future implications based on this data? This is best suited to use supervised learning as models are taught on the dataset to arrive to a desired output. What about the instance you want to solely find relationships within given dataset(s) to further aid your understanding? Well, unsupervised learning can be your answer as it possesses abilities to uncover commonalities and discrepancies amongst the dataset. There even exists semi-supervised learning in the instance your dataset has commonalities of both the previous datasets noted. Of course, there is further division of which algorithms are best suited within this main categories (dependent on your dataset), but you do not have to worry about any of this, we can analyse your data to conclude to an algorithm with reasons why and what the algorithm entails. Want to get started? Contact us now!

Do other solutions easily identify trends and patterns? Are they automated? Are they continuously being improved? Are these other solutions able to be evaluated? Are they catered to your needs? Can these other solutions be applied to multi-dimensional data or data that varies significantly? Tell us your problem now and we can show you if your needs are better suited to being solved via machine learning. You may be surprised upon learning the power of machine learning!