real time commodity risk engine machine learning

#Real time commodity risk engine machine learning serial
#Real time commodity risk engine machine learning update

Some examples of linear regression include: Given patient characteristics, predict the probability of congestive heart failure.Given historical car insurance fraudulent claims and features of the claims such as the age of the claimant, the claimed amount, and the severity of the accident, predict the probability of fraud.Some examples of logistic regression (or other algorithms) include: Classifying a tumor as malignant or not.Predicting patient risk (high-risk patient, low-risk patient).Text sentiment analysis (happy, not happy).Credit card application (good credit, bad credit).Credit card fraud detection (fraud, not fraud).Linear regression predicts a numeric value - for example, the amount of fraud. Logistic regression predicts a probability - for example, the probability of fraud. Classification identifies which category an item belongs to (for example whether a transaction is fraud or not fraud) based on labeled examples of known items (for example, transactions known to be fraud or not). Some examples of predictive modeling are classification and regression. Supervised learning is also called predictive modeling or predictive analytics because you build a model that is capable of making predictions. Supervised algorithms use labeled data in which both the input and target outcome, or label, are provided to the algorithm. Reinforcement learning trains algorithms to maximize rewards based on feedback. Semi-supervised learning uses a mixture of labeled and unlabeled data. Supervised learning algorithms use labeled data and unsupervised learning algorithms find patterns in unlabeled data. In general, machine learning may be broken down into types: supervised, unsupervised, and in between those two. Machine learning uses algorithms to find patterns in data and then uses a model that recognizes those patterns to make predictions on new data. In terms of potential performance, the evolution from the Cray-1 to today’s clusters with lots of GPU’s is roughly a million times what was once the fastest computer on the planet at a tiny fraction of the cost.

#Real time commodity risk engine machine learning serial

A GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously, whereas a CPU consists of a few cores optimized for sequential serial processing. Graphical Processing Units (GPUs) have sped up multi-core servers for parallel processing. Technologies like Apache Spark speed up parallel processing of distributed data even more with iterative algorithms by caching data in-memory across iterations and using lighter weight threads. How Has Analytics Changed in the Last 10 Years?Īccording to Thomas Davenport in the HBR, analytical technology has changed dramatically over the last decade, with more powerful and less expensive distributed computing across commodity servers, streaming analytics, and improved machine learning technologies, enabling companies to store and analyze both far more data and many different types of it. Machine learning has the advantage that it learns from the data, and it can provide data-driven probabilistic predictions.

#Real time commodity risk engine machine learning update

Rules engines have a wide use in industries such as finance and healthcare, and more recently for event processing, but when data is changing, rules can become difficult to update and maintain. Expert systems capture an expert's knowledge in a rules engine.

At the NSA, I even took an MIT video (VCR) class on AI about expert systems. In 1985, when I was a student interning at the NSA, AI was also a very hot topic. AI is an umbrella term (the idea started in the 50s) machine learning is a subset of AI and deep learning is a subset of ML. Throughout the history of AI, the definition has been continuously redefined. This post and future posts are meant to provoke a review of your own data to identify new opportunities. Every piece of information that your business generates has the potential to add value. Machine learning is becoming more accessible to developers, and data scientists work with domain experts, architects, developers, and data engineers, so it is important for everyone to have a good understanding of the possibilities. The goal of this is not to turn you into a data scientist but to give you a better understanding of what you can do with machine learning. In future posts, we will explore vertical use cases. In this post, we will explain what machine learning and deep learning are at a high level with some real-world examples. Deep learning, machine learning, artificial intelligence - all buzzwords that represent the future of analytics.