The main culpritis the speed of development and its incorporation in almost all businesses/industries worldwide. The internet is overwhelmed with webinars, courses, even world-famous universities offer online degrees in this sector. This irrepressible demand also affects all related processes and jobs such as Deep Learning, Neural Networks, Data Science, and Data Analyst.. This attention transformed the global labor market. Most people start their AI journey by practicing Machine Learning model development and further upgrading it later according to their career preferences. Newbies and experts alike experience machine learning models in deployment working well during training that fail in production. Machine Learning in production shows actual data output so when it crashes in that phase, the cause should be investigated in data pre-processing. This is because all ML models are dependent on the quality of datasets incorporated in them. Some of the common reasons why this happens are:
- Incorrect Performance Metric. Choosing valuable performance metrics is an essential part of Machine Learning model training. Mistakes in this part of the process can cause invaluable data outcomes that become a huge waste of time and money.
- Monitoring Deficiency. Omissions in process monitoring will be seen through suspicious and questionable data outcomes. To prevent that, the model must be monitored on a regular daily basis.
- High-dynamic Variables Model Dependency. Dynamic variables change often and it strongly affects the model production phase.
- Complexed Models. Higher predictiveness comes from processing various and complex models but can sometimes decrease the model operability in the production phase
To prevent this, experts made an option for open source machine learning deployment named Open MLOps (Machine Learning Operations) that automates the whole process, making it easier for users.