ODSC - Open Data Science

MAY 10, 2023

Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. Standard data science practices could also be contributing to this issue. Embrace Data-Centric AI The key to unlocking value in AI lies in a data-centric approach, according to Andrew Ng.

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

DataSeries

AUGUST 15, 2024

Data Engineering Career: Unleashing The True Potential of Data Problem-Solving Skills Data Engineers are required to possess strong analytical and problem-solving skills to navigate complex data challenges. Understanding these fundamentals is essential for effective problem-solving in data engineering.

Data Engineering 40

Data Engineering Data Engineering Data Engineering Data Engineer 40

Iguazio

JANUARY 22, 2024

Definitions: Foundation Models, Gen AI, and LLMs Before diving into the practice of productizing LLMs, let’s review the basic definitions of GenAI elements: Foundation Models (FMs) - Large deep learning models that are pre-trained with attention mechanisms on massive datasets. Typically, foundation models are used.

Data Pipeline 52

Data Pipeline ML ML Data Warehouse 52

Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 52

Mlearning.ai

JUNE 16, 2023

Generative AI can be used to automate the data modeling process by generating entity-relationship diagrams or other types of data models and assist in UI design process by generating wireframes or high-fidelity mockups. GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API.

AI 52

AI AI Data Analysis Data Analysis 52

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. Microsoft Azure Synapse Analytics Microsoft Azure Synapse Analytics is an integrated analytics service that combines data warehousing and big data capabilities into a unified platform.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Iguazio

FEBRUARY 8, 2024

Data Pipeline - Manages and processes various data sources. ML Pipeline - Focuses on training, validation and deployment. Application Pipeline - Manages requests and data/model validations. Multi-Stage Pipeline - Ensures correct model behavior and incorporates feedback loops.

ML 52

ML ML Data Scientist AI 52

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

DagsHub

APRIL 21, 2024

MLOps helps these organizations to continuously monitor the systems for accuracy and fairness, with automated processes for model retraining and deployment as new data becomes available. You can consider this stage as the most code-intensive stage of the entire ML pipeline. It is designed to leverage hardware acceleration (e.g.,

Machine Learning 59

Machine Learning Machine Learning ML ML 59

The MLOps Blog

DECEMBER 28, 2022

Team composition The team comprises data pipeline engineers, ML engineers, full-stack engineers, and data scientists. Organization Acquia Industry Software-as-a-service Team size Acquia built an ML team five years ago in 2017 and has a team size of 6.

ML 78

ML ML Data Scientist Machine Learning 78

Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

ODSC - Open Data Science

APRIL 26, 2023

Data Engineer Data engineers are the authors of the infrastructure that stores, processes, and manages the large volumes of data an organization has. The main aspect of their profession is the building and maintenance of data pipelines, which allow for data to move between sources.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

DagsHub

AUGUST 29, 2024

Data can change a lot, models may also quickly evolve and dependencies become old-fashioned which makes it hard to maintain consistency or reproducibility. With weak version control, teams could face problems like inconsistent data, model drift , and clashes in their code. The Need For Reproducibility and Traceability.

ML 52

ML ML Python Machine Learning 52

phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

Data Models 52

Data Models Data Modeling Apache Kafka Data Lakes 52

The MLOps Blog

MAY 9, 2023

A typical machine learning pipeline with various stages highlighted | Source: Author Common types of machine learning pipelines In line with the stages of the ML workflow (data, model, and production), an ML pipeline comprises three different pipelines that solve different workflow stages. Model deployment.

ML 98

ML ML Machine Learning Machine Learning 98

Data Science Dojo

APRIL 3, 2025

You can also read about algorithmic biases and their challenges in fair AI A Strategic Partnership: Databricks and Securitis Gencore AI In the face of these challenges, enterprises strive to balance innovation with security and compliance. Optimized Data Pipelines for AI Readiness AI models are only as good as the data they process.

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AI AI Data Pipeline Data Preparation 195