Data science is becoming more prevalent now, so the typical approach of using local computers is no longer supporting the pace of this fast change. Extending data science work to the cloud provides data scientists with options that will make them more productive with less complication.
At Syntelli, we help companies build the right infrastructure, develop and implement data science solutions that bring business value. Depending on the scale of work, we provide both standard and cloud deployments to companies.
A Standard Data Science Development Process
This approach is handy for small data sizes and less complicated workloads. It is, however, not sustainable for the growing trend and appreciation of data science in business today. Machine learning is gradually moving from just gaining insights into integrating it with daily business operations. This exciting change is also introducing challenges to how data scientists work on local computers. A local computer restricts you to available processing capacity and isolates your work. Getting around this will require continuous investment in resources or extensive coding.
Practical data science is becoming more involved with the need for more data, processing resources, and sophisticated algorithms, so the norm cannot remain business as usual.
With a growing team and the appreciation of data science in an organization, there are certain vital factors to consider for data scientists to be more productive:
Organizations are beginning to expand their data science practice and extend it to other use cases. This development has resulted in having cross-functional teams working together. To maintain a cohesive workflow, there has to be a shift from the traditional coding style to the new age. A collaborative environment will enable models to be reproduced and provide code source control. Data scientists can easily share libraries, collaborate on codes, or review model results securely in a seamless manner that individual computers may not provide.
2. Data Volume
With the prevalent use of data, organizations are collecting more to analyze, which is leading to a continued increase in data volume. Storage is not cheap, but cloud computing offers more affordable alternatives.
You cannot beat the on-demand elastic capacity available with cloud computing. This flexibility is not possible with on-premise servers at the speed and manner you need them. These traditional tools will not serve you well, especially now that time to market is extremely crucial. Not only are they affordable, but they are also managed for you, so you don’t have the staffing overhead cost to maintain infrastructure. Cloud computing, with proper management, gives you more time to focus on more innovative things.
4. Model Metric Tracking
Data scientists need to track their experiments for comparison and reproduction, but with a growing team, this can quickly become very cumbersome without a proper tool in place. It is easy to lose track of changes such as parameters, features, inputs, outputs, as codes change in the process of achieving the optimal model. If this weren’t important, we wouldn’t have software solely dedicated to solving this problem.
Data science code deployment to production is one of the significant challenges with practical data science in organizations. It is usually one of the reasons machine learning models don’t make production. Deployment challenges range from portability, programming language compatibility, and exception management. All of these may be taken care of with meticulous complex codes, but cloud computing a more effective use of time.
Data science is still an evolving field in which we, at Syntelli, are continuously investing time to identify best practices to the benefit of our customers and the community. Please reach out to us for any assistance our team of experts can provide.
Moyosore Lawal, Sr Analytics Associate
Providing solutions that enhance business competitiveness and enable companies achieve their goals leveraging on data is what Moyo stands for. She has worked with data in a number of ways and has a well-grounded understanding of the data lifecycle.
As a Data Scientist/Engineer, she has managed several successful projects building and implementing predictive models. She earned her M.S. in Data Science and Business Analytics degree from the University of North Carolina at Charlotte.
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