May 18, 2022
Machine learning and artificial intelligence are some of the most rapidly developing industries in the entire world right now. The global machine learning market is projected to have mind-boggling growth of many times its size over the next few years. Technologies and tools are evolving quickly and becoming more popular by the day. Machine learning finds its use in analytics, computer vision systems, recommendation engines, and many other fields. Because of this, there is a constantly growing need for models to improve: to learn to do things faster, better, and more efficiently.
One of the essential factors in training a model is providing good data. As one of the most well-known machine learning experts, Andrew Ng, puts it, “If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.” Below, we go over why you should never undervalue the role of the quality of data that is being fed to algorithms.
Data collection and preparation are what most of the efforts in creating a machine learning model go towards. Why? It’s because your machine learning algorithms can only be as good as the data used to train them. Feed an algorithm bad, irrelevant, or faulty data, and it will not be able to find the right solution. The opposite is also true. If high-quality data is used for training a model and everything else is done correctly, the performance of the model will improve and it will be able to solve more and more complex tasks.
Data quality has a direct correlation with how well a model will be able to do its job, how many resources and business hours will be needed to train it, and how the entire project will evolve. This is why it is crucial that you run data quality checks to fill gaps in the data, remove duplicate data, and fix any other anomalies. Doing things as properly as possible might be time-consuming, but it will save the data team exponentially more time by not having to deal with issues that could otherwise arise as the project progresses.
Neglecting or simply not going the extra mile to ensure that the quality of the data used is up to standards can adversely affect the entire machine learning process. Poor data quality can lead to incorrect business intelligence decisions, worse data analysis, and a multitude of errors. Minor problems in the input data going into training a model can turn into large-scale issues at the output. A 2016 study by IBM revealed that $3.1 trillion is lost annually from the U.S. economy due to poor data quality and its effects alone.
Good data is necessary for businesses in competitive industries to stay ahead of the curve and provide the best products and services. No one wants to have their YouTube video taken down due to an algorithm error or their video feed to be full of recommendations that are not relevant. Mistakes like these put people off, and businesses see fewer customers and financial losses.
Most of these issues could be avoided by proactively improving the quality of datasets used in training models. Trying to fix mistakes retroactively is much more complex and time-consuming.
Machine learning models use algorithms to recognize patterns in data and learn from it. There are many metrics to their performance, as there are many tools and resources to improve and optimize it. Unfortunately, no tools and expertise can improve the data model if the input data is incorrect.
The data used in machine learning is the most significant limiting factor for what an ML model can do or how much it can grow. No matter how much you may try to improve the model or how much work and time you and your team put into the process, it will not improve past the level of quality of its data. Data is a hard limit when it comes to machine learning.
If garbage goes in, garbage goes out. Data quality checks are a must when it comes to machine learning. The hard reality is that 60% of the time spent working on machine learning is used to cleaning and organizing data for the algorithms.
Mitigating risks in machine learning projects requires a proactive approach. Unlike other solutions, Masthead doesn’t require direct access to clients’ data. We look into data logs and assess issues before they land in the input data tables. This way, you can detect data errors in real time and handle risks immediately as they arise. Moreover, processing logs does not overload your infrastructure unlike querying data directly, and it also saves you a buck on the cost of cloud infrastructure.
Let’s get in touch and discuss how to get clean data for your machine learning models!
Post Tags :
Data Quality, Google BigQuery, Google Protocol Buffers