Dataconomy

MAY 20, 2025

Data splitting is a fundamental technique in the field of machine learning and data science that allows practitioners to evaluate and improve the performance of their models. Understanding the intricacies of data splitting can significantly influence the robustness and reliability of predictive models.

Machine Learning 91

Machine Learning Machine Learning Data Scientist Data Models 91

Flipboard

JULY 13, 2023

Hello, data enthusiast! In this article let’s discuss “Data Modelling” right from the traditional and classical ways and aligning to today’s digital …

Data Warehouse 123

Data Warehouse Data Models Data Modeling Big Data 123

Data Science Dojo

MARCH 8, 2024

Traditional vs vector databases Data models Traditional databases: They use a relational model that consists of a structured tabular form. Data is contained in tables divided into rows and columns. Hence, the data is well-organized and maintains a well-defined relationship between different entities.

Database 370

Database Natural Language Processing Clustering SQL 370

Flipboard

NOVEMBER 13, 2023

To be successful with a graph database—such as Amazon Neptune, a managed graph database service—you need a graph data model that captures the data you need and can answer your questions efficiently. Building that model is an iterative process.

Data Models 130

Data Models Data Modeling Database AI 130

Data Science Blog

NOVEMBER 15, 2023

New big data architectures and, above all, data sharing concepts such as Data Mesh are ideal for creating a common database for many data products and applications. The Event Log Data Model for Process Mining Process Mining as an analytical system can very well be imagined as an iceberg.

Data Models 245

Data Models Data Modeling Business Intelligence Business Intelligence 245

Hacker News

OCTOBER 19, 2024

Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.

Machine Learning 167

Machine Learning Machine Learning Data Science Data Models 167

Data Science Blog

JULY 23, 2023

Applications of BI, Data Science and Process Mining grow together More and more all these disciplines are growing together as they need to be combined in order to get the best insights. So while Process Mining can be seen as a subpart of BI while both are using Machine Learning for better analytical results.

Data Science 246

Data Science Azure Power BI Business Intelligence 246

Pickl AI

NOVEMBER 29, 2024

Summary: Hydra simplifies process configuration in Machine Learning by dynamically managing parameters, organising configurations hierarchically, and enabling runtime overrides. As the global Machine Learning market, valued at USD 35.80 These issues can hinder experimentation, reproducibility, and workflow efficiency.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Dataconomy

MARCH 5, 2025

Data science platforms are reshaping the landscape of how organizations harness data to drive insights and foster innovation. By providing a comprehensive ecosystem for data professionals, these platforms enhance the capabilities around machine learning, advanced analytics, and collaborative efforts.

Data Science 91

Data Science Data Scientist Machine Learning Machine Learning 91

ODSC - Open Data Science

JUNE 6, 2025

With open open-source machine learning library, NVIDIA cuML, you can achieve significantly higher speed and scale for dimensionality reduction using UMAP without changing any of your code. cuML brings GPU-acceleration to UMAP and HDBSCAN , in addition to scikit-learn algorithms.

Clustering 98

Clustering Machine Learning Machine Learning Algorithm 98

Flipboard

JANUARY 10, 2024

Given that there are so many laptops and laptop configurations out there, we've gone out and found our favorites for data science so you don't have to.

Data Science 155

Data Science Data Models Data Modeling Big Data 155

Dataconomy

JUNE 16, 2025

How structured data works Understanding how structured data operates involves recognizing the role of data models and repositories. These frameworks facilitate the organization and integrity of data across various applications. They represent the structure and constraints that govern how data is stored.

Database 91

Database Data Lakes ETL Natural Language Processing 91

Flipboard

MAY 12, 2023

Data Warehousing 101: A Practical Guide for BeginnersContinue reading on Towards Data Science »

Data Models 131

Data Models Data Modeling Database Data Science 131

Dataconomy

JUNE 17, 2025

In technology and business, entities often represent either real objects or abstract concepts, allowing clarification in data modeling and communication. Named entities and recognition Named entities refer to specific, identifiable units within a set of data, crucial for tasks in data mining and machine learning applications.

Database 91

Database Natural Language Processing Machine Learning Machine Learning 91

Flipboard

FEBRUARY 14, 2023

Time-varying entities may contain multiple time-varying and static attributes, making mapping them a particular challenge.

Data Models 123

Data Models Data Modeling Database Big Data 123

Dataconomy

JUNE 13, 2025

Mechanics of data virtualization Understanding how data virtualization works reveals its benefits in organizations. Middleware role Data virtualization often functions as middleware that bridges various data models and repositories, including cloud data lakes and on-premise warehouses.

Data Visualization 91

Data Visualization Cloud Data Data Lakes Data Warehouse 91

Flipboard

MARCH 8, 2023

When the Navy SEALS embraced its motto, “Slow is smooth, and smooth is fast,” they may as well have been discussing building data models in the same …

Data Models 65

Data Models Data Modeling Big Data Big Data 65

AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

Machine Learning 136

Machine Learning Machine Learning ML ML 136

Dataversity

NOVEMBER 12, 2021

Machine learning (ML) has enabled a whole host of innovations and new business models in fintech, driving breakthroughs in areas such as personalized wealth management, automated fraud detection, and real-time small business accounting tools.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Data Science Dojo

AUGUST 19, 2024

They dive deep into artificial neural networks, algorithms, and data structures, creating groundbreaking solutions for complex issues. These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential.

AI 364

AI AI Machine Learning Machine Learning 364

Heartbeat

JUNE 12, 2023

Source: Author Introduction Machine learning model monitoring tracks the performance and behavior of a machine learning model over time. Organizations can ensure that their machine-learning models remain robust and trustworthy over time by implementing effective model monitoring practices.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

Flipboard

MARCH 28, 2025

Today, we officially take the step to combine the data, models, compute, distribution and talent. xAI and Xs futures are intertwined, Musk wrote in a post on X. This combination will unlock immense potential by blending xAIs advanced AI capability and expertise with Xs massive reach.

Data Models 181

Data Models Data Modeling AI AI 181

Data Science Dojo

JUNE 7, 2023

Data Science is a field that encompasses various disciplines, including statistics, machine learning, and data analysis techniques to extract valuable insights and knowledge from data. It is divided into three primary areas: data preparation, data modeling, and data visualization.

Data Science 195

Data Science Data Visualization Data Scientist Machine Learning 195

Towards AI

NOVEMBER 12, 2024

Sources of Hallucinations: Generalized Training Data: Models trained on non-specialized data may lack depth in healthcare-specific contexts.Probabilistic Generation: LLMs generate text based on probability, which sometimes leads them to select… Read the full blog for free on Medium.

AI 111

AI AI Data Models Data Modeling 111

Dataconomy

JULY 17, 2023

Shared learning enables models to learn from a diverse range of experiences and perspectives, leading to improved performance ( Image Credit ) What is shared learning? This approach helps the student model benefit from the knowledge and generalization abilities of the larger model.

Machine Learning 91

Machine Learning Machine Learning Artificial Intelligence Artificial Intelligence 91

Data Science Dojo

FEBRUARY 23, 2024

GPTs for Data science are the next step towards innovation in various data-related tasks. These are platforms that integrate the field of data analytics with artificial intelligence (AI) and machine learning (ML) solutions. The learning assistance provides deeper insights and improved accuracy.

Data Science 363

Data Science Data Analysis Data Analysis Machine Learning 363

Dataconomy

MARCH 6, 2025

Data vault is not just a method; its an innovative approach to data modeling and integration tailored for modern data warehouses. As businesses continue to evolve, the complexity of managing data efficiently has grown. As businesses continue to evolve, the complexity of managing data efficiently has grown.

Data Warehouse 91

Data Warehouse Data Quality Data Models Data Modeling 91

Data Science Dojo

MARCH 7, 2023

These skills include programming languages such as Python and R, statistics and probability, machine learning, data visualization, and data modeling. These languages are used for data cleaning, manipulation, and analysis, and for building and deploying machine learning models.

Data Scientist 221

Data Scientist Exploratory Data Analysis Data Science Data Visualization 221

Flipboard

NOVEMBER 20, 2024

By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. She leads machine learning projects in various domains such as computer vision, natural language processing, and generative AI.

AWS 160

AWS Natural Language Processing Machine Learning Machine Learning 160

DagsHub

DECEMBER 11, 2023

The following points illustrates some of the main reasons why data versioning is crucial to the success of any data science and machine learning project: Storage space One of the reasons of versioning data is to be able to keep track of multiple versions of the same data which obviously need to be stored as well.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

Flipboard

DECEMBER 22, 2024

Nature Biomedical Engineering - Foundation models can be advantageously harnessed to estimate missing data in multimodal biomedical datasets and to generate realistic synthetic samples.

Data Models 79

Data Models Data Modeling Machine Learning Machine Learning 79

Towards AI

FEBRUARY 7, 2024

In order for us to start using any kind of data logic on this, we need to identify the board location first. Author(s): Ashutosh Malgaonkar Originally published on Towards AI. Here is how tic tac toe looks. So, let us figure out a system to determine board location.

Data Models 109

Data Models Data Modeling Algorithm AI 109

AWS Machine Learning Blog

NOVEMBER 6, 2024

Additionally, consider exploring other AWS services and tools that can complement and enhance your AI-driven applications, such as Amazon SageMaker for machine learning model training and deployment, or Amazon Lex for building conversational interfaces. He is passionate about cloud and machine learning.

AWS 116

AWS Python Machine Learning Machine Learning 116

Flipboard

MAY 12, 2025

Privately adapt models with your data Model customization helps you deliver differentiated and personalized user experiences. To customize models for

AWS 143

AWS Data Models Data Modeling AI 143

Data Science Dojo

APRIL 18, 2023

First, the amount of data available to organizations has grown exponentially in recent years, creating a need for professionals who can make sense of it. Second, advancements in technology, such as big data and machine learning, have made it easier and more efficient to analyze data.

Data Analyst 195

Data Analyst Data Visualization Data Analysis Data Analysis 195

Smart Data Collective

DECEMBER 19, 2021

The rise of machine learning and the use of Artificial Intelligence gradually increases the requirement of data processing. That’s because the machine learning projects go through and process a lot of data, and that data should come in the specified format to make it easier for the AI to catch and process.

Python 141

Python Machine Learning Machine Learning Algorithm 141

Dataconomy

APRIL 2, 2025

With Bitcoin surpassing $87,000 in March 2025, AI and data science have become essential tools in crypto trading, enabling the extraction of meaningful insights from complex market data. AI models used in Bitcoin prediction Different AI models adapt to continuously emerging needs and features of crypto markets.

Data Science 103

Data Science Natural Language Processing Machine Learning Machine Learning 103