Back in 2016, data science platforms are one of the emerging technology trends. In recent studies, the data science platform market is likely to grow by the end of 2021, with North America dominating the industry.
Those who are not familiar with data science platforms will be surprised since it’s a talked-about topic in data science meet-ups, conferences, and publications these days. Remember that this concept is not new, but many still don’t know what a data science platform is all about and why it’s important for companies or businesses.
If you want to learn more about what a data science platform is, read further to learn about the basics, the good features, and why an enterprise data science platform is important for businesses and companies today.
What Is A Data Science Platform?
A data science platform is generally a form of software that merges tools, people, and work products utilized throughout the data science lifecycle–starting from the development up to the deployment phase.
In simple terms, a data science platform can change the way a company works. It’s more than just a simple tool, but a system to organize data and transform team members into a highly efficient unit, with the potential of pivoting and scaling without any misses. Using the right one for your company or business is a transformative move.
The ‘data science lifecycle’ covers three phases. In every phase, it has specific requirements:
- Ideation, exploration, and integration
- Development or experimentation
A data science platform allows better analysis for proper management, monitoring, reproduction, sharing, and deploying analytical models faster. In most cases, all these tasks entail a lot of effort and hassle to establish and maintain models.
With the help of a data science platform, it provides the necessary ‘tools’ to hasten analysis. Additionally, the platform provides a boost to leverage analytics in an effective manner.
What Are The Categories?
- Open data science platform. The open data science platforms offer the flexibility to choose the programming languages and packages to use. It includes the appropriate tools for the right job, depending on the scenario, and allows experimentation with various tools and languages.
- Closed data science platform. In closed data science platforms, you have to utilize the vendor’s specific programming language and the modeling packages and GUI (Graphical User Interface) tools. Sadly, there’s a limit on the tools you can use.
Role Of Data Science Platforms In Businesses And Companies
As the business industry focuses on the importance of business outcomes, this is where an enterprise data science platform is likely to enter the picture.
Back then, data scientists could take on lengthy experimentation tasks using various open-source tools. It wasn’t easy to maintain proper collaboration and it’s rare to accomplish the last deployment step. Today, the inability to achieve the right outcomes can result in a high price than it was in the past. With this in mind, businesses and companies should consider a data science platform.
The market environment for machine learning, data science, and AI can be highly competitive yet fragmented. The complex nature of this industry makes it difficult to fully understand.
A data science and machine learning platform functions as a cohesive software application that provides a combination of basic building blocks that’s vital for creating different kinds of data science solutions and incorporating these solutions into business processes, products, and infrastructure.
Those who mainly use these platforms include citizen data scientists, expert data scientists, data engineers, and machine learning specialists or engineers.
Generally, the ideal enterprise data science platforms focus on:
- Improving data scientists’ productivity level by assisting them with the acceleration and faster delivery of models with minimal error.
- Simplifying work with large volumes and varieties of data
- Delivering reliable, enterprise-grade artificial intelligence that is auditable, bias-free, and reproducible.
Does Your Business Need An Enterprise Data Science Platform?
For businesses or companies who are spending time on basic operations, a data science platform is an ideal solution. If a company couldn’t keep track of the present models or facing lengthy maintenance of previous models, it might be time to consider this platform.
If the collaboration within a team isn’t working, it’s a good sign to use this platform. A good platform can create a logical workflow, promote better integrations, and provide version controls.
In case scaling is the objective and uncertain of how to deploy, a platform is necessary. The majority of the market platforms are specially structured to ease scale and generate better models that require minimal maintenance.
AI Time Journal Staff Writers report on the AI technology advancements and opportunities across industries to leverage AI.