We are living in a data-driven world where progress is made by data rather than relying on intuition or personal experience. Organizations are producing terabytes of data each year and using this data to extract crucial insights, trends, and novel solutions to problems. Data is used to make informed decisions, monitor the health of systems, and get desired results. This enables organizations to make the most out of their money.
With many organizations scaling operations each year, the data generated is also increasing exponentially. Relying on traditional approaches like the trial and error method to handle these enormous amounts of data is senseless. They are based on statistical analysis and are limited to stationary data unlike real data.
Machine learning or ML, however, provides an efficient substitute for these traditional methods. The field of machine learning is growing rapidly and thanks to constant research and development, the results are far more accurate and precise. Machine learning techniques also make things a lot easier for data scientists as a large amount of data can be used at once to make predictions.
What does machine learning testing entail? In simple terms, behavior is fed to the machine learning model as training data. In return, the model outputs the system's logic and makes a prediction based on this logic. However, the logic may or may not satisfy the behavior to the organizations’ expectations. To rectify the situation, machine learning testing comes into play. A model is evaluated by predetermined criteria like its performance, which is measured through metrics such as MSE, MAE.
Machine learning testing is often done in two ways:
Model evaluation is a technique to measure the quality of a model's prediction. It determines how well the model will perform in production. Model evaluation produces several insights such as how well the model is performing on unseen data, how accurate it is, and whether it is overfitting or underfitting or not.
On the other hand, model testing refers to the process of checking the model's performance on the testing dataset. The testing data contains data that is not included either in the training or testing set but follows the same probability distribution as both of them.
Model evaluation and model testing are performed simultaneously in order to achieve high-quality models. However, this is often not enough due to lack of traceability between the model capabilities and the testing.
Since gaining valuable insights hinges on the model’s ability to make accurate predictions, it is important that a strong testing team is employed to validate the model’s performance. The model should be able to fulfill the new needs of the client and results should improve with every modification every time.
In order to do this, the members of the testing team should be capable of the following:
Along with these skills, machine learning testers should be equipped with expertise in data visualization, statistics, probability and data manipulation. They should also be proficient in a powerful programming language such as Python, R, etc.
Machine learning models are hard to evaluate and require the intervention of testers along with developers. Data science testers are specialists who have experience in dealing with large amounts of data and analyzing it. This testing phase also allows the developers to know where the model is making poor results and where the results are being biased.
Similar to software production, testing plays a major role in machine learning projects. Testing allows organizations to not only improve the model's performance and efficiency but reduce cost to the company.
As the demand for skilled data scientists increases in order to create good models, the demand for data science testers will also rise to ensure models’ quality.
In this modern era, we are producing data at a tremendous rate and there is a never-ending need to constantly analyze the same. This data helps organizations expand and maximize their ROI. Data scientists are constantly trying to derive insights and information from the ever-growing to learn about trends and prevent what may be small bottlenecks from turning into big disasters.
Machine learning can be applied to nearly every field and is urgently needed in many, such as healthcare. Among other things, it can help automate the healthcare industry, assist in patient monitoring, aid in the early detection of chronic diseases, and help healthcare professionals make more accurate diagnoses.
Its ability to contribute to the growth and development of most industries gives an idea of just how in-demand machine learning experts are and will continue to be in the near future. It also explains the urgency of learning the required skills to meet the skills gap and build successful careers.
At present, the average salary for a machine learning engineer ranges from USD 74,000 to USD 220,000 while the average salary for a data scientist is around USD 100,000. Money aside, the rewards that come with being at the forefront of machine learning and data science and using them to advance society is next to priceless.
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