What makes data science so difficult is that there isn’t a project workflow that has become standard. A data science project is difficult to work with, as there are many things you need to do to prepare before you even start working with your model. To have a data science workflow that works,a few things must be considered by you and your team.
Data preparation is what makes data science projects. However, there are new tools being developed that make the process as streamlined as software engineering. This has made the industry really excited, as there are many things that we can learn.
Focusing on the Data
The problem with building a data science project is that it requires a lot of data from many different sources. The typical data science workflow involves scientists wrangling and manipulating that data until they can run algorithms on it. The data can come from many different places, meaning that you might need a lot of technologies to get it into where you want to process it. That further adds another layer of complexity to the equation.
There needs to be a comprehensive system that provides a simple flowchart that takes you from the data you have to the finished model. You can get something like this with xpresso.ai. What such a solution does is to help your project workflow by helping you take a data centric approach.
Making the Process As Simple As Possible
The structure of your data has such a profound impact on whether your data science project will be successful or not, asthe majority of your effort will be spent on that process. A data centric approach has many benefits, but the most important one is that it will help you streamline the data pipeline, among other things.
When you can streamline that pipeline, you can automate a significant part of the process of developing data needed to feed your models. The inspiration from solutions like these comes from software engineering. In software engineering, the goal is to go from source code to a finished program that does everything the end-user wants. When you take that approach, your data science workflow becomes a lot simpler.
Other Data Science Workflow Tools
Despite how crucial data is to the machine learning process, there are still many other tools that impact the workflow. For example, MLOps also involves data version control and the traditional version control you see in software engineering.
The way you look at the data science workflow will depend on your background. For example, machine learning engineers will only be concerned with the model and the rest of the traditional software engineering process. That is why having an end-to-end solution is crucial for companies trying to be successful in this endeavor. That type of solution ensures that everything is done according to the principles set out forMLOps. It also ensures that your data science project doesn’t stall because of unforeseen issues.