Modern data science and machine learning platforms (DSML) today are constantly evolving with advanced features and functionality to meet new demands of innovative markets. Gartner recently published a report, evaluating 19 of the top DSML platforms in the space across 15 critical capabilities to help data and analytics leaders make the right choice for their business.
Let’s look at the four use cases identified by Gartner, to see how we hold up against the competition.
Business exploration
Before you even start digging into your data and launching major analytics projects, you need to get a better understanding of the data you have. Business exploration means using data exploration, data preparation, and data visualization tools to identify business problems and opportunities to improve.
According to Gartner, “Business exploration, which is key for expert data scientists and citizen data scientists, has been accelerated and made more accessible by augmented analytics.”
Part of this push for augmented analytics comes from the growing trend of automated machine learning. Using Spotfire® Data Science you can automate key steps of the machine learning process and iteratively learn from the data to optimize performance. Without any coding, you can put your computers to work finding patterns and insights for you.
Advanced prototyping
Solving business problems with advanced prototyping usually involves applying several machine learning and other advanced analytic techniques to models in new, innovative ways. Gartner sees this as an area that increasingly relies on open source. Luckily, the top machine learning platforms today, are flexible and seamlessly integrate with open source languages such as R, Python, as well as frameworks like Amazon SageMaker, Google TensorFlow, and Azure Machine Learning.
Product refinement
The ability to deploy machine learning models into production, monitor performance, and automatically update and refresh hyperparameters is critical for any data science and machine learning initiative. Gartner states in the report that, “Production refinement is more vital than ever to machine learning as organizations mature around operationalization and data, and analytics leaders demand tangible ROI.”
Uncovering valuable insights in the data isn’t enough anymore. With Spotfire Data Science, you can take action on those insights in real-time to operationalize artificial intelligence, machine learning, and data science and gain a competitive advantage with your data.
Nontraditional data science
While nontraditional data science is a fairly new use case, tools in this area are becoming increasingly popular among citizen data scientists and developers.
With an intuitive, easy-to-understand user interface, Spotfire Data Science is for everyone. Data science today is a team sport. And to fully realize the value of data science and operationalize machine learning models within your organization, you need to involve and collaborate with cross-functional teams.
In the report, Gartner recommends data and analytics leaders, “Choose the best-fit platform based on balancing the desired mix of use cases, available user skill sets, deployment environment, and prioritized strength of critical capabilities.” To find the right data science and machine learning platform for your business, download a complimentary copy of the report here.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.