Machine learning brings 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 producing an incredible volume of data and statistics which is impossible for humans to analyze manually. Machine learning technology helps combine all the data gathered from myriad touch points for delivering useful insights to enterprises that contribute to the various strategic outcomes.
For most businesses, machine learning seems close to rocket science, appearing expensive and talent demanding. And, if you are aiming at building another recommendation system, it really is. But the trend of making everything-as-a-service has affected this sophisticated sphere, too. You can jump-start a machine learning initiative without much investment, which would be the right move if you are new to data science and just want to grab the low hanging fruit.
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 at cloud scale. Faststream technologies Machine Learning services fully support open source technologies.
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 ML 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.