Source: analyticsindiamag.com
If your business is struggling to reduce operational costs during the ongoing economic crisis or maintain the efficiency of services or the quality products, then Data Science as a Service (DSaaS) should be used to solve these issues.
DSaaS is an ideal choice for businesses to manage without a large team of data scientists and analysts in-house. It provides companies access to analytics resources for particular data science demands without much expense on building such teams from scratch.
Companies gain advantages based on their capability to cause data-driven decisions more efficiently and faster than their opponents. Data solely gives limited value to companies without the expertise, tools, and knowledge to comprehend what questions to ask, how to reveal the right patterns, and the skills to make forecasts that point to profitable action.
Data Science As A Service: How Does It Work?
DSaaS is mostly a cloud-based delivery model, where different tools for data analytics are provided and can be configured by the user to process and analyse enormous quantities of heterogeneous data efficiently.
Customers will serve their enterprise data into the platform and get back more valuable analytics insights. These analytic insights are produced by analytical apps, which harmonise analytic data workflows. These workflows are created using a collection of services that perform analytical algorithms. Many of them are based on machine learning concepts. Also, the data contributed by the user can be improved by external, curated data sources.
Once the clients upload the data to the platform or cloud database, the data scientist as a service platform can be incorporated with data engineers who will work on the uploaded data. There are mostly subscription-based models.
There are multiple data science consulting firms, startups and even bigger cloud platforms which provide data science as a service offering in varied forms. As a part of DSaaS, meticulous delivery of production-ready predictive models, and data analysis can be generated using mature methodologies.
Examples
Such offerings included high-quality and complex analytics solutions which will turn your raw data into quantifiable information, without customers having to spend money on specialised data science teams.
For example, a recent partnership between Snowflake and Zepl highlighted the importance of data science as a service. Using Zepl’s new native Snowflake integration, small data science teams can rapidly explore, analyse and collaborate around Snowflake’s cloud-built data warehouse. Within minutes, Zepl makes machine learning at scale to Snowflake data across entire data science teams. Zepl’s powerful collaboration capabilities are used by data scientists, data engineers, data analysts, team managers and executives globally for data science needs.
DSaaS offerings also exist for specific industry domains. For example, Cogitativo, a Berkeley, California-based company for healthcare service organisations, raised $18.5 million in Series B recently. The funding round was led by Wells Fargo Strategic Capital. Cogitativo implements a machine learning platform for healthcare performance enhancements by allowing clients to recognise and solve healthcare system complexities. Currently, about 50 healthcare companies with over 45 million members utilise the company’s solutions to control their operational and strategic challenges, and their capacity to drive marketplace complexity.
Then, there are also plug-and-play data science and AI solutions which aim at providing analytical expertise underpinned by the data scientist’s expertise. But such plug-and-play machine learning and AI tools can remain enablers of analytic force, not the origin of it. For that, teams may still need data scientists to bring a variety of abilities to the task, leading among them the capability to wrangle messy data. This is where a small team of data scientists may still be needed.
Amazon Kendra, an AI-enabled enterprise search tool, responds to queries by searching through a variety of data sources within a company. The search tool can be performed on websites and interfaces such as chatbots. Kendra uses deep learning models to learn from text from multiple sources and across several domains, including life sciences and legal and financial service without ML/AI experts and data scientists.