ODSC - Open Data Science

JUNE 7, 2023

Wednesday, June 14th Me, my health, and AI: applications in medical diagnostics and prognostics: Sara Khalid | Associate Professor, Senior Research Fellow, Biomedical Data Science and Health Informatics | University of Oxford Iterated and Exponentially Weighted Moving Principal Component Analysis : Dr. Paul A.

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ODSC - Open Data Science

JULY 22, 2023

Keynotes Our main keynote sessions were held on the virtual side of the conference.

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ODSC - Open Data Science

JULY 14, 2023

The session participants will learn the theory behind compound sparsity, state-of-the-art techniques, and how to apply it in practice using the Neural Magic platform. You’ll also discuss different popular large language models and compare the techniques and accuracy of results among different large language models.

Machine Learning 52

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Pickl AI

AUGUST 9, 2024

Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers. Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis.

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DagsHub

OCTOBER 23, 2024

Apache Kafka Apache Kafka is a distributed event streaming platform for real-time data pipelines and stream processing. Kafka is highly scalable and ideal for high-throughput and low-latency data pipeline applications. Data Processing Tools These tools are essential for handling large volumes of unstructured data.

Machine Learning 52

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Pickl AI

JULY 20, 2023

Real-time Data Stream Analysis: Use Python with libraries like Apache Kafka and Apache Spark to process and analyze real-time data streams from sources like Twitter, sensors, or website logs. Image Recognition with Deep Learning: Use Python with TensorFlow or PyTorch to build an image recognition model (e.g.,

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The MLOps Blog

AUGUST 11, 2023

Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., Scikit-learn, Feature Tools) 4 Model Training (e.g., Scikit-learn, MLflow) 6 Model Deployment (e.g., Other areas in ML pipelines: transfer learning, anomaly detection, vector similarity search, clustering, etc. 1 Data Ingestion (e.g.,

ML 52

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