Applied Machine Learning: Platforms & Scenarios

Scenario

Machine Learning is fast becoming a reliable and scalable set of technologies that can be applied to many business sectors, providing the ability to automate processes and to make applications more intelligent. Among the various technologies that are part of the Machine Learning world, Deep Learning is gaining popularity, extending its scope of application to artificial intelligence.

Reply’s vision

Machine Learning Reply is the new Reply Company focused on Machine Learning and Artificial Intelligence: from open source libraries to big players’ platforms, from Deep Learning to Cognitive Computing, from Data Robotics to conversational bots (chatbots), Machine Learning Reply applies new outcomes of artificial intelligence research to real sector usage scenarios.

Solution

Machine Learning is a scientific discipline that deals with the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.

Machine Learning techniques apply when knowledge is not enough to code, there is the need to scale for the huge amount of data, program has to adapt its behavior or solution changes in time.

There are three types of Machine Learning: supervised, unsupervised and reinforcement learning.

Supervised learning is task driven: algorithm predicts the behavior of an agent, using the past experience (i.e. Regression/Classification). Unsupervised learning is data driven: algorithm discovers similarities and hidden structures inside the data (i.e. Clustering). Reinforcement learning is environment driven: algorithm learns to react to an environment and to have smart behaviors.

Machine Learning Reply is focused on designed the best model according to data understanding (that involves subject matter experts) and data preparation (carried on by Machine Learning Reply Data Scientists).

There are several applications of Machine Learning, i.e.: Data Robotics, Recommendation Systems, Chatbots and Predictive Engines.

Data Robotics deals with engines that learn how to perform specific actions basing on historical data (learning set) and continuous learning (feedbacks).

Recommendation Systems aim to predict user preferences learning from user and community behavior.

A chatbot is like a chat contact, but instead of talking to another person, it’s the bot that’s in the conversation. Bots can fetch the news, check the weather, play with photos, start a game, order a pizza or a taxi for you.

Predictive Engines leverage historical data to produce predictive models in order to be applied in scenarios such as Sales Prediction, Promotions Targeting, Customer Segmentation, Demand Forecasting, Price Optimization, Social Network Analysis and Predictive Maintenance.