THE IMPORTANCE OF DATA
The model is at the core of all machine learning activities. We develop, monitor, deploy, improve, and test a model, but it always has to be backed by relevant data, so it is preferable to focus on improving data rather than the underlying model.
We can help you make sense of data that is dirty, unstructured, and hard to access by transforming it into meaningful information. We will collect raw data, examine, and segment it, and then deliver it in an understandable format so that it can be aggregated to generate insights.
QUALITY DATA SAMPLING
The standard data science approach explains that more data leads to better machine learning models, but we have to remember the Garbage In Garbage Out principle!
It is not just the volume of data, but the quality of the data that contributes to the development of better performing machine learning models. Any machine learning model that attempts to learn a pattern involving too noisy or not sufficiently varied features will be rendered ineffective, regardless of the amount of data.
More data is not always better, and there are cases where less data would be preferable or more desirable. More data can also introduce unforeseen expenses which are not justified by the benefits. Small datasets can be prepared to be sufficient to answer the question of interest, and collecting additional data wouldn't increase practical time or financial burdens.
Data analysis and pipelines services involve selecting quality samples, quality descriptors and attributes, complementary data sources, labelling evaluation, and proposing data structures that enable suitable algorithms.
LET'S GET YOUR PROJECT STARTED!