Anomaly detection using deep learning to measure the quality of large datasets
February 22, 2020
Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data.
Anomaly detection in smart buildings using federated learning
February 21, 2020
There's an exponential growth in the number of internet-enabled devices on modern smart buildings. IoT sensors measure temperature, lighting, IP camera, and more. Tuhin Sharma and Bargava Subramanian explain how they built anomaly-detection models using federated learningwhich is privacy preserving and doesn't require data to be moved to the cloudfor data quality and cybersecurity.
Running AI workloads in containers (sponsored by BMC Software)
February 10, 2020
Developing, deploying and managing AI and anomaly detection models is tough business. See-Kit Lam details how Malwarebytes has leveraged containerization, scheduling, and orchestration to build a behavioral detection platform and a pipeline to bring models from concept to production.
Scalable anomaly detection with Spark and SOS
February 10, 2020
Jeroen Janssens dives into stochastic outlier section (SOS), an unsupervised algorithm for detecting anomalies in large, high-dimensional data. SOS has been implemented in Python, R, and, most recently, Spark. He illustrates the idea and intuition behind SOS, demonstrates the implementation of SOS on top of Spark, and applies SOS to a real-world use case.
ThirdEye: LinkedIns business-wide monitoring platform
February 8, 2020
Failures or issues in a product or service can negatively affect the business. Detecting issues in advance and recovering from them is crucial to keeping the business alive. Join Akshay Rai to learn more about LinkedIn's next-generation open source monitoring platform, an integrated solution for real-time alerting and collaborative analysis.
LSTM-based time series anomaly detection using Analytics Zoo for Spark and BigDL
January 8, 2020
Collecting and processing massive time series data (e.g., logs, sensor readings, etc.) and detecting the anomalies in real time is critical for many emerging smart systems, such as industrial, manufacturing, AIOps, and the IoT. Guoqiong Song explains how to detect anomalies in time series data using Analytics Zoo and BigDL at scale on a standard Spark cluster.
Practicing data science: A collection of case studies
January 7, 2020
Rosaria Silipo shares a collection of past data science projects. While the structure is often similardata collection, data transformation, model training, deploymenteach required its own special trick, whether a change in perspective or a particular technique to deal with special case and special business questions.
Real-time SQL stream processing at scale with Apache Kafka and KSQL
January 7, 2020
Robin Moffatt walks you through the architectural reasoning for Apache Kafka and the benefits of real-time integration. You'll then build a streaming data pipeline using nothing but your bare hands, Kafka Connect, and KSQL.
Analytics Zoo: Distributed TensorFlow and Keras on Apache Spark
December 27, 2019
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zooa unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipelineusing real-world use cases from JD.com, MLSListings, the World Bank, Baosight, and Midea/KUKA.
How to determine the optimal anomaly detection method for your application
December 23, 2019
Anomaly detection has many applications, such as tracking business KPIs or fraud spotting in credit card transactions. Unfortunately, there's no one best way to detect anomalies across a variety of domains. Jonathan Merriman and Cynthia Freeman introduce a framework to determine the best anomaly detection method for the application based on time series characteristics.