In software projects, data scientists often need to deliver more than some machine learning modules in the backend. Ther
Cloud computing is a significant trend for the big data and AI industry in 2020. Using cloud environments like AWS, Microsoft Azure, or Google Cloud makes it quick and straightforward to deploy AI-powered software modules and integrate them with operational software. Therefore, data scientists need to acquire to develop AI and big data solutions on top of cloud infrastructure.
One of the areas in DevOps widely used software projects is continuous integration / continuous delivery (CI/CD). Without the basic knowledge level required to work with CI/CD pipelines, data scientists cannot effectively collaborate with software teams. In the meantime, becoming proficient in cloud computing makes it easier for data scientists to learn and leverage the already existing DevOps capabilities within the cloud infrastructure and this helps the data scientists to adapt their skills faster to the requirements of working in production environments.
Pouyan has done his Ph.D. research work on predictive modeling of consumer decision making and remains interested in developing state-of-the-art solutions in machine learning and artificial intelligence.
As a Data Scientist, you cannot simply start a project without getting the green light from stakeholders first. Some of the stakeholders will not understand Data Science concepts and processes at all — until you explain them well. It is up to you to prove to them that a process could occur including automation and prediction.
The ability to work with structured and unstructured database technologies is also a necessity for data scientists within software projects. These database technologies can include SQL databases like PostgreSQL or NoSQL databases like MongoDB. Databases are so widely used in any software system that there is almost no escape from data science projects. There are also advanced big data technologies like Spark and Hive that enable working with Hadoop clusters.
For example, you want to start a project that will help the company classify clothing items quickly on an e-commerce website. In order to get ‘buy-in’, or proof that this process will be beneficial, you will have to outline the process, the expected resources need, and likely results.
Pouyan is also leading the Data Science Circle team to build a career hub between employers and data science talents. DSC’s mission is to nurture the next generation of data scientists through career training and helping the employers to find top talents in big data.
To summarize this process, you could create a visualization that better describes the proposed process, as well as the timeline involved. There are several ways to approach this visualization. You could create a proof-of-concept by utilizing products like PowerPoint, Google Slides, or some more involved products including Jira, Lucid Charts, Draw.io, and ProductPlan.
Pouyan has done his Ph.D. research work on predictive modeling of consumer decision making and remains interested in developing state-of-the-art solutions in machine learning and artificial intelligence.
For data scientists, learning DevOps is essential. DevOps can be used to optimize the deployment of software components related to data pipelines, model training, model testing, model predictions, and model deployment. Ensuring DevOps best practices are the core of functional big data pipelines in the production environment, whether on-premise or cloud.
Pouyan R. Fard is the Founder