Real Time Bidding using Machine Learning
The AdTech world has been totally reinvented a few years ago with the birth of real time auction technologies, known as Real-Time Bidding (RTB). Those auctions allow to buy ad inventory impression by impression. For each visit of a user on a publisher website, each advertiser can choose to display an ad or not and find the right maximum price he is willing to pay to buy this opportunity. Consequently, we see an increasing need of automation and optimisation for the players connected to the RTB and a lot of solutions make use of Machine Learning. The involved datasets are big (billions of lines per day) and they evolve very quickly. Thus it’s challenging to be able to train models every few hours to only use up to date data in production. Furthermore, those models need to be easily improvable through feature selection and hyper parameter tuning. This requires the ability to run offline and online tests.
Model-Based System Engineering
Model-based systems engineering intends to centralize all information about the system in a model, often called the “single source of truth.” The model supports the system’s entire life cycle from requirements documentation to validation and verification exercises to maintenance and training purposes, just to mention some. Stakeholders like decision makers and suppliers as well as the development teams can access the model at different views and levels of detail, to access data according to their needs while consistency of the information is guaranteed. The approach requires a sophisticated common modeling environment, including tools, good practices and industry standards that are currently investigated by applying MBSE methods on a smaller scale during the development of selected avionics systems.
Computer Vision and Robotics for precision Agriculture
Both in open-air and greenhouse conditions, the most widely used practice in pest and disease control is to uniformly spray pesticides over the cropping area. To be effective, this approach requires significant amounts of pesticides which results in a high financial and significant environmental cost. ML is used as a part of the general precision agriculture management, where agro-chemicals input is targeted in terms of time, place and affected plants. Apart from diseases, weeds are the most important threats to crop production. The biggest problem in weeds fighting is that they are difficult to detect and discriminate from crops. Computer vision and ML algorithms can improve detection and discrimination of weeds at low cost and with no environmental issues and side effects. In future, these technologies will drive robots that will destroy weeds, minimizing the need for herbicides.
Reinforcement Learning for Autonomous Vehicles
Reinforcement learning (RL) is a machine learning technique that attempts to learn a strategy, called a policy, that optimizes an objective for an agent acting in an environment. For example, the agent might be a robot, the environment might be a maze, and the goal might be to successfully navigate the maze in the smallest amount of time. In RL, the agent takes an action, observes the state of the environment, and gets a reward based on the value of the current state of the environment. The goal is to maximize the long-term reward that the agent receives as a result of its actions. RL is well-suited for solving problems where an agent can make autonomous decisions.
The AI/ML system is the brain of a Prescriptive maintenance platform. ML models for production machines are designed to detect anomalous behavior during production. Training data helps to develop specific models for individual recipe steps or specific equipment types. Timely detections and prescriptions are accomplished by determining, on a continuous basis, whether a data point falls outside these bounds it is flagged as an anomaly and reported. Capability to train a new model and deploy on demand is the key. In doing so, the model is able to learn on and adapt over time.
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