Machine Learning development services

Machine Learning (ML) automatically recognises complex, previously unknown and useful information in all types of data. In the ML process, a model learns by looking for patterns hidden within given data. The more data there is, the more accurately the model resembles the real process. Additionally, by adjusting model parameters we can further improve its performance. Having an adequate model built, we can then generalise its application and make predictions about fresh data.

There are two common types of ML tasks. Firstly, classification that predicts output based on given data. This can be used, for example, for a credit scoring or product dement prediction. The second one type clustering that assigns similar objects together, which makes it a perfect solution for customer or user segmentation.

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This is an iterative process of building Machine Learning solutions in a pipeline. Generally speaking, it consists of the following steps: data wrangling, model building and making predictions. The model is a simplified representation of a modeled process. The data phase includes data collection, data exploration, feature engineering, and splitting data into training and evaluation sets. After that, models are built, evaluated, and their performance is improved by hyperparameter tuning. With the model deployed, we can make predictions using new data. The last phase in the project lifecycle is model maintenance and support.

Features or variables are measurable characteristics of the object (like a product or a customer) and are a basic input building block of all datasets. Based on them, prediction or clustering is performed. During the feature engineering process, new features can be obtained from raw features with the aim to improve an algorithm’s performance.

Natural Language Processing (NLP) is an automatic manipulation and understanding of written or spoken text. It intersects such fields as linguistics, artificial intelligence and machine learning.

Recommendation Systems are algorithms used for suggesting relevant content or products for users or shoppers. Recommendations are generated on user-item attribute similarities.

Predictive Analytics models in business applications use patterns found in historical data to identify future risks and opportunities. Apart from machine learning techniques, predictive analytics also encompasses statistics and data mining. One of its well-known applications is credit scoring used in the financial sector.

Deep Learning (DL) relates to a subgroup of Artificial Neural Networks (ANN) that have more than 3 layers and therefore can extract higher-level features. ANN are a part of Artificial Intelligence that solve complex and non-linear problems by mimicking animal neuron network behaviour. DL systems are self-teaching and able to filter information through multiple hidden layers in order to resemble human brain processes with an even higher accuracy. It is an implementation of artificial intelligence and can be used e.g. for automatic machine translation, image classification, voice recognition or self-driving cars.

Cloud-based modelling merges Machine Learning capabilities with cloud-based computing environments. Cloud-based modelling can be performed using AWS SageMaker, Microsoft Azure, Google Cloud ML Engine or IBM Watson.

Although ML can be applied almost everywhere, there are some limitations we have to be aware of. It requires a large amount of high-quality data to perform well and deliver reliable solutions. There is always some bias as we are working only on an available subset of the data that might not fully represent the modelled process. There is also an ethical dilemma with a responsibility for the outcome of ML-based decisions (e.g. a self-driving car accident). In some cases, a simple interpretability of modelling outcomes may not be possible.