Advance your digital transformation and optimize operations with machine learning.

Transform pipeline integrity management through an enterprise SaaS based solution and start positively impacting your bottom line.

Machine learning (ML) and Data Science (DS) are important emerging technologies that have the potential to transform organizations. Exponential increases in computational capacity, the emergence of cloud computing, and innovations in algorithms have resulted in tremendous advances in the application of ML. Leveraging ML and DS techniques, organizations can unlock tremendous value through improved customer service, streamlined operations, and the realization of new business models.

What is Machine Learning?

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.

Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.

How Machine Learning works?

  1. A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labelled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
  2. An Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this evaluate and optimize process, updating weights autonomously until a threshold of accuracy has been met.

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