While enticing, the promised land of machine learning and data sciences is still mysterious for many. That makes ML and data science a boiling pot of execution challenges ML and data scientists face in project research and development. With Qwak, let’s discuss the 10 biggest ML and data science challenges.
But before that, are machine learning and data sciences answers to all problems? Let’s discuss this as the first of the 10 biggest ML and data science challenges.
ML/AI and Data Sciences for every problem?
Machine learning and data science are the fascinating sciences of every business, which are often keen to opt or pursue a particular problem statement with an ML solution. Machine learning does bring in a lot of potential and value, but not everything requires its application.
Most problems can easily be solved with easy exploratory data analysis. It is crucial to recognize the right use cases that need the heavy artillery of ML.
Hype and Expectations
With the increasing and built-up hype of ML, the hopes and prospects have been set high. Whereas the other technological advancements are explained in their potential and capabilities parameters, ML needs to be explained in terms of its limitations.
Marketing and media promise the moon, while the reality is quite different. ML is a complex technology that needs time to implement and fully leverage. Similarly, it also requires a lot of resources while delivering ROI. This is why data scientists need to manage the expectations from the beginning.
Input and Output Co-dependency
ML models are not magic crystals and can’t predict perfectly if the data is insufficient. A few rows maintained in a spreadsheet won’t be able to drive any actionable insights.
To develop a model that provides desired business outcomes, data science teams will need to ask for far more relevant data. Through enhancements like augmented data management, data scientists can figure out how to leverage the data.
Maintaining Balance between Accuracy & Practicality
Next on the 10 biggest ML and data science challenges list is the balance of accuracy and practicality. The blind chase after accuracy can ruin other aspects of a project.
There has always been a persisting demand and expectation of highly accurate models; however, in chasing after the top accuracy, many businesses often forget other factors like engineering costs and simplicity.
For instance, the most accurate model for Netflix – which won a million-dollar prize, was not implemented. Rather, another model offering a good mix of accuracy, stability, simplicity and interpretation was adopted for implementation.
Robustness of ML Models
Owing to the high amount of resources and time put in by data scientists required to build ML models, it is often asked if the models have learned all they ever need to. One of the 10 biggest ML and data science challenges is that an ML model continually needs to be trained, ensuring that it remains future-ready. Thus businesses must always incorporate such costs when they begin an ML project.
What’s more on the 10 biggest ML and data science challenges list?
Lack or Absence of Data
The lack of sufficient and actionable data is one of the biggest challenges to data science projects. For building algorithms and models, sufficient and actionable data is a requirement. In the absence of data, it can be difficult and rather impossible to complete a data science project. Any problems might need data sets that are internal and readily available and the external ones that are needed to be bought or collected. The absence of different data types might result in data bias, which leads to suboptimal results.
It is pivotal to determine the data sources – both external and internal, which will then be used to train the models.
Data complexity is another major of the 10 biggest ML and data science challenges. It includes data that is unstructured, missing a lot of values or is noisy; this could lead to a difficult accurate building of algorithms and models. Additionally, data inaccuracies and inconsistency can further aid complexity. The non-stationary data can make the complexity bigger and more difficult to work with. These points mean that data changes/iterates over time, making it difficult to build models that accurately predict future data points.
In certain cases, you might not have enough labeled data to effectively train your machine learning models. This can become one of the 10 biggest ML and data science challenges, as labeling data is an expensive process that also takes a lot of time. You might have to hire a resource to label data or use data labeling services.
What requires data labeling?
Problems related to image classification, document classification, and object detection might need data labeling.
Data Privacy Challenges
When working with data, you must consider and take precautions regarding data privacy. This also includes ensuring that the sensitive and critical data can’t be released without consent and that the data is not used for illegal purposes.
Data privacy becomes a challenge when working with datasets containing sensitive information. While data scientists working together have different levels of experience or belong to different organizations, data privacy concerns escalate.
The last of the 10 biggest ML and data science challenges are needing to have models which work better with precision and are accurate. Achieving this can be difficult if the available data is not high quality or is not big enough in size.
Such situations require setting up model governance and data management processes. One of the primary aspects is the set up of processes and tools in place for regular model retraining – if needed. Retaining can be a result of data changes or for improving model performance. Re-training models are expensive and can become time-consuming. Plus, it is also difficult to retrain larger models that are trained on a lot of data.
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