Machine Learning Techniques

Platforms & Scenarios

What is Machine Learning?

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 is quickly 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. Especially Deep Learning technology is gaining popularity among the various Machine Learning approaches, as it facilitates the development of Artificial Intelligence opening up whole new fields of application. Reply supports companies to leverage the potential of Machine Learning and Artificial intelligence to deploy industry and business specific solutions such as: Data Robotics, chatbots or predictive engines.

Machine Learning is a key enabling technology.
How can I actually use it in a way that makes sense?

Data Robotics

Data Robotics deals with engines that learn how to perform specific actions basing on historical data (learning set) and continuous learning (feedbacks). Data Robotics, a new level of process automation based on self-learning technologies and AI, aims to improve productivity and efficiency. Traditional robotics paradigms meet with innovative smart engines providing the opportunity to automate activities and procedures as never before. A Data Robot is the artificial intelligence able to analyze situations, understand complex information, learn and operate optimizing and supporting human activity. Following a learn-by-error approach it is able to gain knowledge to elaborate transactions, manipulate data and improve system integration.

Recommendation Systems

Recommendation Systems aim to predict user preferences learning from user and community behavior. These systems are used in Ecommerce to recommend “you might also like” products, in advertising to show you the ads you are most likely receptive for, your favourite video or music streaming platform to present content that you are likely to enjoy based on your consumption history and insights gained from all users with similar preferences.


A chatbot is a software capable of interfacing, on the one hand, with the end-user, relying on natural language, and on the other hand, with information systems, with the aim of supporting the company in its various functions using an efficient and innovative approach. But it is not just about technology. A chatbot is a new, customer-oriented communication channel that uses machine learning tools to connect the company with its stakeholders. But they can do more: when fed with industry specific information and the information about the customer, chatbots can provide individualized advice and support in various scenarios, be it the Banking Robo Advisor, aware of your financial situation and spending behavior or the Car Presales assistant, that knows your favorite color and what you are looking for in a car (discover Reply Chatbot for Automotive).

Predictive Engines

Predictive Engines leverage historical data to produce predictive models in order to be applied in Business Intelligence scenarios such as Sales Prediction, Demand & Revenue Forecasting or for Promotions Targeting, Customer Segmentation, Price Optimization, Social Network Analysis. Predictive Engines are also employed in connection with cyber-physical systems, e.g. in an Industry 4.0 context, where they help to improve the Overall Equipment Efficiency (OEE) by predicting the best time for maintenance based on the equipment’s actual performance.

Reply: Verticalised Machine Learning Competence

Reply unites in its network structure vast expertise and experience in all related fields, from Big Data, User Experience Design, Digital Marketing, Financial Services sector, Automotive Industry, Artificial Intelligence to Industry 4.0. This ensures, that Reply can adopt new technologies like Machine Learning to a wide range of verticals or business scenarios and support companies in developing innovative solutions and new business models.

The Reply group even incorporates a highly focused company, Machine Learning Reply , specialising in 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. 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).