AWS Machine Learning Blog

JANUARY 26, 2023

After the first training job is complete, the instances used for training are retained in the warm pool cluster. Likewise, if more training jobs come in with instance type, instance count, volume & networking criteria similar to the warm pool cluster resources, then the matched instances will be used for running the jobs.

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ML ML Data Scientist AWS 101

AWS Machine Learning Blog

MAY 3, 2023

Furthermore, the dynamic nature of a customer’s data can also result in a large variance of the processing time and resources required to optimally complete the feature engineering. AWS customer Vericast is a marketing solutions company that makes data-driven decisions to boost marketing ROIs for its clients.

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AWS Machine Learning Machine Learning ML 105

AWS Machine Learning Blog

AUGUST 22, 2023

Many ML algorithms train over large datasets, generalizing patterns it finds in the data and inferring results from those patterns as new unseen records are processed. With SageMaker, data scientists and developers can quickly build and train ML models, and then deploy them into a production-ready hosted environment.

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Machine Learning Machine Learning AWS ML 98

AWS Machine Learning Blog

MAY 15, 2023

We outline how we built an automated demand forecasting pipeline using Forecast and orchestrated by AWS Step Functions to predict daily demand for SKUs. On an ongoing basis, we calculate mean absolute percentage error (MAPE) ratios with product-based data, and optimize model and feature ingestion processes.

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Algorithm Data Scientist Machine Learning Machine Learning 99

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

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Pickl AI

JANUARY 30, 2025

Careful planning mitigates data skew, debugging complexities, and memory constraints. Embracing MapReduce ensures fault tolerance, faster insights, and cost-effective big data analytics. The framework simultaneously sorts these key-value pairs to facilitate grouped data in readiness for the Reducer.

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Big Data Big Data Hadoop AWS 52

IBM Journey to AI blog

SEPTEMBER 4, 2024

Speed Kafka’s data processing system uses APIs in a unique way that help it to optimize data integration to many other database storage designs, such as the popular SQL and NoSQL architectures , used for big data analytics.

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Pickl AI

JULY 20, 2023

Defining clear objectives and selecting appropriate techniques to extract valuable insights from the data is essential. Here are some project ideas suitable for students interested in big data analytics with Python: 1.

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Analytics Analytics Big Data Big Data 40

DagsHub

NOVEMBER 11, 2024

Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data. These capture the semantic relationships between words, facilitating tasks like classification and clustering within ETL pipelines. This will ensure the data is in an ideal structure for further analysis.

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AI AI Data Lakes Database 52

The MLOps Blog

AUGUST 11, 2023

As usage increased, the system had to be scaled vertically, approaching AWS instance-type limits. Embeddings provide a lower-dimensional representation of high-dimensional data that retains key patterns and information. Other areas in ML pipelines: transfer learning, anomaly detection, vector similarity search, clustering, etc.

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ML ML Machine Learning Machine Learning 52

Flipboard

MAY 15, 2025

With SageMaker Lakehouse, you can access tables stored in Amazon Redshift managed storage (RMS) through Iceberg APIs, using the Iceberg REST catalog backed by AWS Glue Data Catalog. This expands your data integration workload across data lakes and data warehouses, enabling seamless access to diverse data sources.

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AWS Machine Learning Blog

DECEMBER 2, 2024

We’re planning to migrate to Amazon Bedrock Knowledge Bases to eliminate cluster management and add extensibility to our pipeline. He specializes in leveraging generative AI and Big Data analytics to design, develop, and implement secure, scalable, innovative solutions that empowers Finance Operations with better productivity, automation.

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AI AI Big Data Big Data 108

Pickl AI

FEBRUARY 23, 2025

Summary: Big Data tools empower organizations to analyze vast datasets, leading to improved decision-making and operational efficiency. Ultimately, leveraging Big Data analytics provides a competitive advantage and drives innovation across various industries.

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Big Data Big Data Apache Hadoop Apache Kafka 52