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Challenges Data Scientists face everyday
Data science and machine learning are popular terms right now on the internet and the trend is growing. With a large volume of data in various formats, companies are increasingly relying on data scientists, machine learning engineers and software developers to automate the process of various mundane tasks and improve the productivity and efficiency in which operations are carried out both in the short and long-terms. Furthermore, the salaries of data scientists and ML engineers is also increasing further with good compensation and stock benefits.
However, it should also be taken into consideration that data scientists often face a lot of challenges in their work starting from data extraction to deploying the best hyperparameter-tuned model on a large scale. Therefore, being aware of these challenges and learning how to tackle them could have a significant impact in the way at which the work is done with ease with less effort. Highlighted below are some of the challenges that data scientists face during their work along with a few tips and strategies about tackling them.
Data is available everywhere in various formats such as in the form of texts, videos, audios, images and websites. According to the estimates provided by seedscientific.com, the amount of data available in the world is a staggering 44 zettabytes at the dawn of 2020. The number is even higher for the present year and will also tend to grow in the future as well. With this vast information, it is implicit that making the best use of it by analyzing the trends and getting to know the predictions would be handy for companies so that they have taken appropriate steps to ensure that they are moving in the right direction and making profits.
After taking a look at the challenges that are detailed below, a data scientist can gather all the tools and resources that are needed to tackle the challenges and make useful contribution to the company.
Finding the right data
The challenge with having vast amounts of data is in finding the right data that could be used by the team so that they generate valuable patterns and insights from it. It is important to ask questions such as who should get what data along with whether there should be constant stream of data to be used for analysis or if the data is fixed. Asking these interesting questions can ease up the task of making the data science workflow along with designing the system less tedious and easy to follow.
There can be data that contains a lot of outliers, missing values or inaccurate information that impacts the performance of the machine learning models. Hence it is also important to preprocess the data so that the models perform optimally and efficiently along with a good increase in their performance.
One of the challenges that data scientists must consider is to prepare the vast amount of data and make it accessible and interpretable to other members of the team along with providing useful insights and patterns on their own. Preprocessing the data also helps in increasing its readability so that other members from the team can go over the features from the data. There are cases where various features from data might be having outliers which must be treated as not all the machine learning models are robust to them. In addition to this, there can also be features that contain missing values or incorrect values which must be identified so that they do not decrease the performance of the ML models that would be ready to be deployed in production. All of these things could be identified with the help of exploratory data analysis (EDA) that is often the first step in machine learning when dealing with large amount of data. Therefore, this step must be initially followed to ensure that we get the best results from our models respectively.
Choosing the Right Performance Metric
With a large number of metrics available in machine learning, it is possible to get caught up in the loop and not be able to decide the best tools or metrics which could be used for evaluation. For the classification problems, we have popular metrics such as accuracy, precision, recall and f1-score along with others.
For the regression tasks, there are other metrics that we must take into consideration such as the mean squared error or the mean absolute error. In the case of time series problems which is also mostly a regression task, we taken other metrics such as the mean absolute percentage error (MAPE) or also the root mean squared error. Choosing the right metric therefore could be a challenge that a data scientist or a machine learning engineer must deal with to be more productive and ensure that the company is getting the best results due to this analysis.
After taking the data and preprocessing it and ensuring that it is performing well on the cross-validation data, it is now time to deploy it and put it to production. After all, it would not be useful if the model is just giving the right predictions without showing the results on the test data or the data that it has not seen before. Therefore, deploying the models in production should also be taken into consideration.
Sometimes the infrastructure that is used to run these models should also be considered when trying to deploy the models in real-time. If we want a low-latency systems with one of the popular uses in internet applications, choosing the ML models that give results quickly can be a good thing that could be taken into consideration. There are other systems where the latency requirement might not be so stringent. Some of the applications involve Netflix recommendation system of movies. In this system, it is not always necessary to give recommendations within a very short span of time. The model can take a day or two to gather more insights from a particular user of interest along with other users before coming up with robust recommendations. Therefore, considering the business problem under hand before deployment is necessary.
As a machine learning engineer, it is important to monitor the performance of the models in production. There can always be a scope for improvement in terms of latency, efficiency and scope of the project. There can also be possible situations where the models become dysfunctional or can product skewed results based on the new data. Therefore, constant monitoring and retraining of the models can be one of the challenges that a machine learning engineer must handle.
Reducing the dimensionality of the data can also be a good step to monitor the performance of the system and see if there is a large reduction in accuracy or mean squared error depending on whether the ML problem is a classification or a regression problem.
All in all, we’ve seen how machine learning could be used and the challenges that are associated in the machine learning workflow. Taking a look at these challenges, data scientists can ensure that they have the right tools and resources to tackle them and give valuable insights to the companies.
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