Data Science Dojo

JULY 6, 2023

It supports various data types and offers advanced features like data sharing and multi-cluster warehouses. Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS). It is known for its high performance and cost-effectiveness.

Data Engineering 195

Data Engineering Data Engineering Data Engineering Data Engineer 195

Towards AI

APRIL 28, 2025

Tests setup We ran load tests on an Amazon EKS cluster using t2.medium medium instances (2 vCPUs, 4 GB RAM), hosting both the Locust deployment and the Ray cluster running Volga. Each Ray pod was mapped to a single EKS node to ensure resource isolation.

Clustering 59

Clustering AWS ML ML 59

AWS Machine Learning Blog

APRIL 25, 2024

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. Solution overview Amazon Transcribe is the go-to service for speaker diarization in AWS. Hugging Face is a popular open source hub for machine learning (ML) models.

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AWS ML ML Python 125

AWS Machine Learning Blog

SEPTEMBER 19, 2024

In this post, AWS collaborates with Meta’s PyTorch team to showcase how you can use Meta’s torchtune library to fine-tune Meta Llama-like architectures while using a fully-managed environment provided by Amazon SageMaker Training. SageMaker Training is a comprehensive, fully managed ML service that enables scalable model training.

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AWS ML ML Machine Learning 98

AWS Machine Learning Blog

AUGUST 30, 2024

With the rapid growth of generative artificial intelligence (AI), many AWS customers are looking to take advantage of publicly available foundation models (FMs) and technologies. This includes Meta Llama 3, Meta’s publicly available large language model (LLM).

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SQL AWS Database AI 93

AWS Machine Learning Blog

NOVEMBER 15, 2023

An AutoML tool applies a combination of different algorithms and various preprocessing techniques to your data. For example, it can scale the data, perform univariate feature selection, conduct PCA at different variance threshold levels, and apply clustering. Analyze the results and deploy the best-performing model.

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Algorithm AWS ML ML 122

Women in Big Data

NOVEMBER 27, 2024

By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making. How to Choose a Data Warehouse for Your Big Data Choosing a data warehouse for big data storage necessitates a thorough assessment of your unique requirements.

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Data Warehouse Big Data Big Data Azure 59

O'Reilly Media

OCTOBER 19, 2021

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? ML use cases rarely dictate the master data management solution, so the ML stack needs to integrate with existing data warehouses.

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ML ML Data Scientist AWS 145

Cambridge Intelligence

OCTOBER 19, 2023

Multi-model databases combine graphs with two other NoSQL data models – document and key-value stores. RDF vs property graphs Another way to categorize graph databases is by their data structure. RDF vs property graphs Another way to categorize graph databases is by their data structure.

Database 59

Database Azure Analytics Analytics 59

phData

FEBRUARY 13, 2024

By centralizing SAP ERP data in Snowflake, organizations can gain deeper insights into key business metrics, trends, and performance indicators, enabling more informed decision-making, strategic planning, and operational optimization. Violations of license restrictions can result in penalties, additional fees, or even legal consequences.

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Analytics Analytics Data Scientist Data Models 59

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Read More: Advanced SQL Tips and Tricks for Data Analysts.

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ETL Data Quality Data Pipeline Data Warehouse 40

Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

NOVEMBER 28, 2024

Unsupervised Learning Unsupervised learning involves training models on data without labels, where the system tries to find hidden patterns or structures. This type of learning is used when labelled data is scarce or unavailable. Scalability Considerations Scalability is a key concern in model deployment.

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Machine Learning Machine Learning ML ML 52

Pickl AI

JULY 25, 2023

Model Development Data Scientists develop sophisticated machine-learning models to derive valuable insights and predictions from the data. These models may include regression, classification, clustering, and more. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

DagsHub

NOVEMBER 11, 2024

In this article, we’ll explore how AI can transform unstructured data into actionable intelligence, empowering you to make informed decisions, enhance customer experiences, and stay ahead of the competition. What is Unstructured Data? Word2Vec , GloVe , and BERT are good sources of embedding generation for textual data.

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AI AI Data Lakes Database 52

The MLOps Blog

JUNE 27, 2023

For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services. SageMaker Studio offers built-in algorithms, automated model tuning, and seamless integration with AWS services, making it a powerful platform for developing and deploying machine learning solutions at scale.

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Machine Learning Machine Learning ML ML 134

DagsHub

OCTOBER 28, 2024

The gateway is designed to handle both internal LLMs (like Llama, Falcon, or models fine-tuned in-house) and external APIs (such as OpenAI, Google, or AWS Bedrock). LLM Gateways can enforce security policies, encrypt sensitive information, and manage access control to protect data. Your team only needs to learn one system.

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ML ML AWS AI 52

DagsHub

OCTOBER 23, 2024

NoSQL Databases NoSQL databases do not follow the traditional relational database structure, which makes them ideal for storing unstructured data. They allow flexible data models such as document, key-value, and wide-column formats, which are well-suited for large-scale data management.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

DagsHub

APRIL 21, 2024

Scikit-learn provides a consistent API for training and using machine learning models, making it easy to experiment with different algorithms and techniques. It also provides tools for model evaluation , including cross-validation, hyperparameter tuning, and metrics such as accuracy, precision, recall, and F1-score.

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Machine Learning Machine Learning ML ML 59

The MLOps Blog

MARCH 15, 2023

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. Server update locks the entire cluster. It supports multiple file formats.

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Data Pipeline ETL SQL Data Quality 59

The MLOps Blog

JANUARY 25, 2023

A multilingual embedding model is an effective tool that combines semantic information for language understanding with the ability to encode text from various languages into a common embedding space. This allows it to be used for a variety of downstream tasks, including text classification, clustering, and others.

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Natural Language Processing ML ML Deep Learning 52

The MLOps Blog

MAY 9, 2023

3 Quickly build and deploy an end-to-end ML pipeline with Kubeflow Pipelines on AWS. They include: 1 Data (or input) pipeline. 2 Model (or training) pipeline. Pre-requisites In this demo, you will use MiniKF to set up Kubeflow on AWS. If you don’t already have an AWS account, create one. Kale v0.7.0.

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ML ML Machine Learning Machine Learning 98

The MLOps Blog

OCTOBER 3, 2023

It’s almost like a specialized data processing and storage solution. For example, you can use BigQuery , AWS , or Azure. It can be a cluster run by Kubernetes or maybe something else. Mikiko Bazeley: Yeah, so we actually did a talk at Data Council. How awful are they?” They’re terrible people.

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ML ML Data Scientist Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 22, 2024

Although QLoRA helps optimize memory during fine-tuning, we will use Amazon SageMaker Training to spin up a resilient training cluster, manage orchestration, and monitor the cluster for failures. To take complete advantage of this multi-GPU cluster, we use the recent support of QLoRA and PyTorch FSDP. 24xlarge compute instance.

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Clustering AWS ML ML 99

AWS Machine Learning Blog

FEBRUARY 3, 2025

The user in this example has uploaded a number of videos, including some recordings of AWS re:Invent talks. The frontend posts the file to an application S3 bucket, at which point a file processing flow is initiated through a triggered AWS Lambda. The citation should include the source file name and timestamp.

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AWS AI AI Database 114

AWS Machine Learning Blog

OCTOBER 11, 2024

With the Amazon Bedrock serverless experience, you can experiment with and evaluate top foundation models (FMs) for your use cases, privately customize them with your data using techniques such as fine-tuning and RAG, and build agents that run tasks using enterprise systems and data sources.

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Database AWS Clustering Data Lakes 107