QuestDB is an open-source time-series database engineered for low latency. It uses a column-oriented, time-partitioned storage engine with memory-mapped files and vectorized execution for high-throughput ingestion and millisecond-level analytical queries. SQL is extended with time-series operators such as SAMPLE BY, LATEST ON, ASOF JOIN, WINDOW JOIN and HORIZON JOIN.
BigML
BigML is a machine learning platform for classification, regression, time series forecasting, cluster analysis, anomaly detection, association discovery, topic modeling and principal component analysis. Models can be used for predictions through remote services or exported and embedded into applications. A three-day free trial gives access to all functionality.
Features
- solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis Source: bigml.com
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0QuestDB is an open-source time-series database engineered for low latency. It uses a column-oriented, time-partitioned storage engine with memory-mapped files and vectorized execution for high-throughput ingestion and millisecond-level analytical queries. SQL is extended with time-series operators such as SAMPLE BY, LATEST ON, ASOF JOIN, WINDOW JOIN and HORIZON JOIN.
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0ELKI is an open source data mining software written in Java. It focuses on research in algorithms, emphasizing unsupervised methods in cluster analysis and outlier detection, and offers data index structures for performance and scalability. It is designed for researchers and students to extend.
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Matt Harrison is a personal blog authored by Matt Harrison. The blog covers topics including Amazon book piracy, the Effective Pandas book, Python and data science courses, and general software development content.
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0A blog and article site by Meta S. Brown about predictive analytics. It addresses executives, business analysts, IT staff and data analysts, describing how each group can understand analytics, work with analysts, and communicate technical topics to decision makers.
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0ML-Ops is a site about Machine Learning Operations. It covers motivation for MLOps, designing ML-powered software, the end-to-end ML workflow lifecycle, three levels of ML-based software, MLOps principles, CRISP-ML(Q), the MLOps Stack Canvas and ML model governance. It describes MLOps as aiming to unify the release cycle for machine learning and software.
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0A weekly news and interview podcast covering the Kubernetes community, including release discussions, projects, and AI policy topics. Episodes feature guests from the Kubernetes ecosystem and are hosted as numbered installments with accompanying show notes.
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