Businesses thrive to reduce the learning curve and accelerate the adoption of visualization in their organization. With our machine learning platform as a service, it becomes easy to leverage an entire ecosystem that a machine learning platform provides. Our best-in-breed machine learning platform ensures data consistency and high-level workflows that ultimately lead to higher productivity of the organization.
Our ML platform allows you to import data into the system from different data sources. Types of data you can upload in our ML platform can be in the form of spreadsheets, CSV files, and many others.
Data cleansing helps you focus more on the areas where data needs more attention. Cleaning involves removing data that might distort the analysis. Our ML platform allows you to easily remove the unwanted data, and sort and standardize the format.
Our ML platform will enable users to check the data quality and consistency issues. Data validation allows users to see whether the sorted data has been properly addressed by applied transformations.
Feature engineering helps to increase the predictive power of the learning algorithm. Our ML platform enables users to identify business values required to create intelligence.
On the basis of the pre-trained data set and the categories in which its observations are classified, users can identify to which category the new set of data belongs. Our ML platform provides with classification that deals with defining sets of observations and adding new observations in respective stacks depending on the parameters.
Our ML platform consists of state-of-the-art algorithms to help users identify relationship among several variables. These algorithms are used to perform regression analysis on your data.
With predictive analytics, users will be able to view insights and based on that, they will be able to forecast desired parameters. Our ML platform will enable users to select previous models to run algorithms on current datasets to get similar results.
Data management allows teams to share, discover, and use a highly curated set of features that address data-related problems. From sources like Spark, SAP, spreadsheets, and other tools, data is collected and managed under a single system.
With large-scale distributed training of decision trees, linear and logistic models, time series models, and deep neural networks, the ML platform enables users to scale from small datasets to billions of samples for quick iterations.
The ML model once trained is ready to be deployed. Deployment refers to building an analytical model that processes new incoming data in real-time. Based on the set of historic data, the ML platform then understands and analyzes new datasets that are introduced in real-time.
Once models are deployed, they are used to make predictions based on feature data loaded from a dataset. With ML platform, users can correlate data to predict certain aspects of their business like sales, machine failure, and other operations.
IDC predicts that in the next two years 30% of major retailers will adopt a retail omnichannel commerce platform that integrates a data analytics layer.
The overall artificial intelligence (AI) in healthcare market was valued at USD 667.1 million in 2016 and is expected to reach USD 7,988.8 million by 2022, at a CAGR of 52.68% between 2017 and 2022. – PR Newswire
Venture capitalists and banks invested around $5 billion dollars in AI and ML in 2016. – McKinsey.
63.5% telecom companies, worldwide, are making new technology investments for AI systems. – IDC Research