KERNELBAY

Kernelbay completed a Machine Learning Development project for a global IT Service Provider company. The project aimed to enrich a dataset with environmental data and analyze the dependencies between initial and enriched parts to create a Machine Learning model for sales prescriptions. Kernelbay was chosen for their ready-to-use augmented analytics solution that fit the company's use case. The project involved enriching the dataset with a broad range of parameters defining the environment at the time and location of each sales transaction. Kernelbay provided correlations, insights, patterns, and anomalies and built the Machine Learning model for sales prescriptions using a forecast of environmental parameters. The team consisted of a data engineer and two Machine Learning engineers.

Kernelbay also developed a Data & Analytics project for a software company that produces a cloud platform for crop monitoring. The project aimed to develop a model of yield loss estimation and monitoring based on a series of vegetation maps of the field. Kernelbay proposed that the prediction could be improved by enriching the simple vegetation activities data with additional parameters, such as temperature, wind strength, soil moisture, and atmospheric pressure. The project stages included uploading the initial dataset, retrieving synthetic data from images, enriching it with environment data, data preprocessing, applying Machine Learning algorithms and statistical methods, and developing a forecasting service. The Kernelbay team consisted of a data analyst, a data engineer, and a data scientist.