Original article was published by on AI Magazine
Collaboration between data scientists, IT, business analysts & developers drive organisation’s success
The focus on business outcomes has taken on a technological twist. Organisations relying on emerging trends in technology have a sole motive, ‘To drive the company towards growth.’ As the embrace of innovation continues, it takes a step further for advanced systems to be employed in routine work.
Earlier, it was okay for data scientists to get dragged into vague tasks or time-consuming experimentation with a variety of open-source tools in the name of innovation. The collaboration was often an afterthought or extremely difficult to achieve across the enterprise. Deployment of models in the enterprise was considered as a rarely achieved step. However, the table has turned today. Not accomplishing these tasks and acquiring a data science driven outcome has a greater cost of loss than it did previously. Henceforth, now is the best time to consider a data science platform for improving enterprises.
Focus of an enterprise data platform
Since the invasion of technology in the working landscape, data science, machine learning and AI has fragmented competitiveness in the field. Gartner defines a data science and machine learning platform as a cohesive software application that offers a mix of basic building blocks essential for creating many kinds of data science solutions and incorporating such solutions into business processes, surrounding infrastructure and products.
Remarkably, the primary users of data science and machine learning platform are people specialized in certain fields such as data scientists, data engineers, citizen data scientists and machine learning engineers. Data science platform works to minimize their job while bringing up the company’s revenue.
Here are some of the aims of data science platform,
• Data science platforms make data scientists more productive by aiding them to deliver models faster with less error.
• It makes the job easy for data scientists to work with larger volumes and varieties of data.
• These platforms deliver trusted and enterprise-grade AU that is bias-free, audible and reproducible.
Using open source at data science platform
Data scientists are not people who directly became experts in what they do. They were once starters who struggled to achieve even simple things in data science. During the initial stages of learning, they used open-source tools to learn and function at data science platforms. Henceforth, it is highly possible for them look for similar open-source tools even after moving to an enterprise role. Sole reliance on open-source too has its flaws like,
• Difficulty managing different tools with different releases
• Complications that arise with sharing code and sharing models
• Governance and security issues
• Time and cost involved in integrating and maintaining these tools
• Difficulty in deploying machine learning models into business dashboards and systems
Data scientists can’t get entangled in all these issues and prolong the functioning process. Henceforth, the best possible way for data scientists to use open-source tools without trouble is by adopting a data science platform where the system offers managed access to open-source tools and libraries.
By using data science platforms, data scientists no longer have to rely on IT to set up or maintain their preferred tools. Team collaboration between IT, business analysts, and developers are also important while dealing with larger data science life cycle. An effective data science platform will ensure that machine learning models can be consistently operationalised across the enterprise. The data from diverse sources such as on-premise data, in the cloud, and hybrid management environments can be shared and used productively by the team.
Data science platform in action
Collaboration between data scientists, IT, business analysts and developers is essential to drive productivity and business outcomes. Data science platform acts as a right collaborative source with the following features,
• Data ingestion
• Data preparation
• Data exploration
• Feature engineering
• Model creation and training
• Model testing
Data science platform is increasingly utilizing its abilities to make changes and working system simple in various sectors. An Israeli agriculture company is successfully using a cloud-based data science platform to monitor its crops. It analyzes data captured by drones that fly over the fields. The captured images are uploaded into the cloud. Using machine learning, the data is analyzed and recognizes the places where there are pest attacks. The farmers can specifically focus on that place to control the pest spread.
Share This Article
Do the sharing thingy