AWS Machine Learning Blog

NOVEMBER 3, 2023

This process comprises two key components: event data and optical tracking data. Event data collection entails gathering the fundamental building blocks of the game. For the precision needed in shot speed calculations, we must ensure that the ball’s position aligns precisely with the moment of the event.

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AWS Apache Kafka Data Scientist Data Science 113

AWS Machine Learning Blog

APRIL 19, 2023

We use Amazon SageMaker to train a model using the built-in XGBoost algorithm on aggregated features created from historical transactions. An event time feature is also required, which enables the feature store to track the history of feature values over time. The feature groups for our use case are shown in the following table.

ML 90

ML ML Apache Kafka SQL 90

Pickl AI

SEPTEMBER 18, 2024

It utilises Amazon Web Services (AWS) as its main data lake, processing over 550 billion events daily—equivalent to approximately 1.3 Data in Motion Technologies like Apache Kafka facilitate real-time processing of events and data, allowing Netflix to respond swiftly to user interactions and operational needs.

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Big Data Big Data Apache Kafka Big Data Analytics 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

The application, once deployed, constructs an ML model using the Random Cut Forest (RCF) algorithm. It initially sources input time series data from Amazon Managed Streaming for Apache Kafka (Amazon MSK) using this live stream for model training. You can follow him on linkedin, syedfurqhan Nirmal Kumar is Sr.

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Pickl AI

AUGUST 9, 2024

Data Streaming Learning about real-time data collection methods using tools like Apache Kafka and Amazon Kinesis. Students should understand the concepts of event-driven architecture and stream processing. Students should learn how to leverage Machine Learning algorithms to extract insights from large datasets.

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Big Data Big Data Big Data Analytics Big Data Analytics 52

phData

SEPTEMBER 27, 2024

In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more. Rich Context: Each event carries with it a wealth of contextual information. What is Activity Schema Modeling?

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Pickl AI

JULY 20, 2023

Diagnostic Analytics Projects: Diagnostic analytics seeks to determine the reasons behind specific events or patterns observed in the data. 3. Predictive Analytics Projects: Predictive analytics involves using historical data to predict future events or outcomes. Root cause analysis is a typical diagnostic analytics task.

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Analytics Analytics Big Data Big Data 40

DagsHub

JANUARY 14, 2025

Similar Audio: Audio recordings of the same event or sound but with different microphone placements or background noise. The extent and nature of the impact depend on several factors, including the proportion of duplicates, the type of duplicates (exact or near), the learning algorithm used, and the specific use case.

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Machine Learning Machine Learning Clustering Algorithm 52

DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Flexibility: Airflow was designed with batch workflows in mind; it was not meant for permanently running event-based workflows.

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Machine Learning Machine Learning ML ML 59

The MLOps Blog

MARCH 15, 2023

Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven. The logical flow of running upstream and downstream tasks is decided using an algorithm commonly known as a Directed Acyclic Graph (DAG). Pricing Up to a million events/month on the free plan.

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Data Pipeline ETL SQL Data Quality 59

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. It allows unstructured data to be moved and processed easily between systems.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

FEBRUARY 23, 2025

These tools leverage advanced algorithms and methodologies to process large datasets, uncovering valuable insights that can drive strategic decision-making. Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently.

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Big Data Big Data Apache Hadoop Apache Kafka 52