Machine learning Solutions bring new insights every day across a broad range of industries and research worldwide. Be part of it and explore the best of what happens when human and machine intelligence is combined. The ever-increasing usage of electronic means of interaction and commerce, as well as IoT devices, produces an incredible volume of data and statistics which is impossible for humans to analyze manually. Our technology helps combine all the data gathered from myriad touchpoints for delivering useful insights to enterprises that contribute to the various strategic outcomes.
It is an integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models on a cloud-scale.
In today’s rapidly evolving business landscape, harnessing the power of data is essential for staying ahead of the competition. Faststream, a pioneer in innovative solutions, is proud to present cutting-edge Machine Learning Solutions designed to revolutionize the way businesses operate.
Machine Learning (ML) is a transformative technology that empowers systems to learn from data, identify patterns, and make intelligent decisions without explicit programming. Faststream’s ML Solutions enables businesses to extract valuable insights, automate processes, and make data-driven decisions for enhanced efficiency and competitiveness.
Classify the problem: Build your problem taxonomy that describes how to classify the problem or business question to solve.
Acquire data: Identify where the data exists to support the problem you’re trying to solve. Data used in Machine Learning can come from a variety of sources, such as ERP systems, IoT edge devices, or mainframe data.
Process data: Identify how to prepare data for Machine Learning execution. Steps here include data transformation, normalization, and cleansing, as well as the selection of training sets.
Model the problem: Determine the Machine Learning algorithms to be used for training or clustering. A range of algorithms can be acquired and extended to suit different purposes.
Validate and execute: Validate results, determine the platform to execute models and algorithms, and then execute the Machine Learning routines. The execution process likely comprises many cycles of running the Machine Learning routine and tuning and refining results.
Deploy: Finally, the output of the Machine Learning process is deployed to provide some form of business value. This value may come in the form of data that will inform decisions, feed applications or systems, or be stored for future analysis.
