Machine Learning (ML) systems are complex, and this complexity increases the chances of failure as well. Knowing what may go wrong is critical for developing robust machine learning systems.
There are a number of foreseeable reasons why machine learning initiatives fail, many of which may be avoided with the right knowledge and diligence. Here are some of the most common challenges that machine learning projects face, as well as ways to prevent them.
Access to appropriate data is a problem for many projects
All AI/ML endeavors require data, which is needed for testing, training, and operating models. However, acquiring such data is a stumbling block because most organizational data is dispersed among on-premises and cloud data repositories, each with its own set of compliance and quality control standards, making data consolidation and analysis that much more complex.
Another stumbling block is data silos. When teams use multiple systems to store and handle data sets, data silos — collections of data controlled by one team but not completely available to others – can form. That might, however, be a result of a siloed organizational structure.
In reality, no one knows everything. It is critical to have at least one ML expert on the team, to be able to do the foundational work, for the successful adoption and implementation of ML in enterprise projects. Being overly confident, without the right skill, sets in the team will only add to the chances of failure.
Excessive reliance on observational data
Organizations are nearly drowning in large volumes of observational data. Thanks to developments in technology such as integrated smart devices and telematics as well as relatively inexpensive and available big data storage and a desire to incorporate more data science into business decisions. However, a high level of data availability might result in observational data dumpster diving.
When adopting a strong tool like machine learning, it pays to be more aware about what organizations are searching for. Businesses should take advantage of their large observational data resources to uncover potentially valuable insights, but evaluate those hypotheses through AB or multivariate testing to distinguish reality from fiction.
Lacking the appropriate yardstick
The ability to evaluate the overall performance of a trained model is crucial in machine learning. It’s critical to assess how well the model performs when compared to both training and test data. This data will be used to choose the model to use, the hyper-parameters to utilize, and decide if the model is ready for production use.
It is vital to select the right assessment measures for the job at hand when evaluating model performance.
Failure to work with the operations team
Machine learning has become more accessible in various ways. There are far more machine learning tools available today than there were even a few years ago, and data science knowledge has multiplied.
Having a data science team to work on an AI and ML project in isolation, on the other hand, might drive the organization down the most difficult path to success. They may come across unanticipated difficulties unless they have prior familiarity with them. Unfortunately, they can also get into the thick of a project before recognizing they are not adequately prepared.
It’s imperative to make sure that domain specialists like process engineers and plant operators are not left out of the process because they are familiar with its complexity and the context of relevant data.