Machine Learning Development

The problem of data cleanliness and ordering may not be systematized, but you definitely do not want the ML system to draw conclusions based on data that is not connected in any way in real life. Therefore, before feeding data to the system, data engineers must structure the underlying data. If you arrange the data incorrectly, then the ML system can make conclusions, which will not have any connection with the real behavior of your processes or clients.

You need a lot of data to train an ML model. The more data you have, the better the results will be. This means that in most cases, with a small amount of data, training the ML model is impossible. Consider this if you are a small or medium size business that does not have access to large databases and does not aggregate user or customer data.

The process of developing, configuring and training an ML algorithm requires highly qualified specialists who are not very easy to find, given the booming market demand for machine learning. Evaluate the qualifications of your engineering partners in advance, and do not expect to save on the development. ML development requires significant investments, but the results are worth it.

Data modeling and ML testing need to re-produce real-life infrastructure environments. How would you know the obtained results are good if you don’t have enough infrastructure to imitate the environment? This is a common problem for those companies that want to apply the results of models trained on other businesses’ or publicly open datasets. Make sure you have all the technical requirements to test the results.