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

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 172

Machine Learning Machine Learning Data Science Data Modeling 172

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

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

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 139

Machine Learning Machine Learning ML ML 139

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

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

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

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

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 Modeling Data Models Big Data 123

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

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 Modeling 131

Data Modeling Data Models Database AI 131

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

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

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

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 121

AWS Python Machine Learning Machine Learning 121

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

Towards AI

AUGUST 8, 2023

After the required line it does not add any information and only increases the computation cost Also, low SR can cause a loss of information Points to remember — While training all audio samples to have the same sampling rate If you are using a pre-trained model, the audio sample should be resampled to match the SR of the audio data model trained on (..)

Machine Learning 98

Machine Learning Machine Learning Data Models Data Modeling 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 156

Data Science Data Modeling Data Models Big Data 156

IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? What is machine learning?

Machine Learning 70

Machine Learning Machine Learning Data Science Big Data 70

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

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

ODSC - Open Data Science

JANUARY 25, 2023

Resilient machine learning systems are fast, accurate, and flexible. Continue reading to learn more about Azure ML’s latest announcements. The two steps to building resilient matching learning systems. Speed improvements in ML workflow When choosing a machine learning cloud platform, speed is top-of-mind.

Machine Learning 59

Machine Learning Machine Learning Azure ML 59

AWS Machine Learning Blog

DECEMBER 9, 2024

With access to a wide range of generative AI foundation models (FM) and the ability to build and train their own machine learning (ML) models in Amazon SageMaker , users want a seamless and secure way to experiment with and select the models that deliver the most value for their business.

AWS 105

AWS ML ML Data Scientist 105

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

Flipboard

MAY 12, 2023

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

Data Modeling 132

Data Modeling Data Models Database Data Science 132

Flipboard

FEBRUARY 14, 2023

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

Data Modeling 123

Data Modeling Data Models Database Big Data 123

Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.

Machine Learning 52

Machine Learning Machine Learning Support Vector Machines Decision Trees 52

Dataconomy

MAY 2, 2023

What do machine learning engineers do? They design, develop, and deploy the machine learning algorithms that power everything from self-driving cars to personalized recommendations. What do machine learning engineers do? Does a machine learning engineer do coding? They build the future.

ML 110

ML ML Machine Learning Machine Learning 110

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

Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Data Science Connect

JULY 21, 2024

Key features include ArangoGraph Cloud for scalable deployment, ArangoDB Visualizer for data navigation, and ArangoGraphML for machine learning applications. The platform offers flexibility, scalability, and performance for handling complex data models, making it a versatile solution for diverse industries.

Machine Learning 100

Machine Learning Machine Learning Database Data Models 100

Smart Data Collective

APRIL 5, 2022

They are using tools like Amazon SageMaker to take advantage of more powerful machine learning capabilities. Amazon SageMaker is a hardware accelerator platform that uses cloud-based machine learning technology. IBM Watson Studio is a very popular solution for handling machine learning and data science tasks.

Data Scientist 145

Data Scientist Data Science Machine Learning Machine Learning 145

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 Modeling 65

Data Modeling Data Models Big Data Big Data 65

DagsHub

OCTOBER 14, 2024

Machine learning workflows are a collection of multiple critical stages including data collection, preprocessing, model development, training, evaluation, and deployment. can significantly change the model performance. Why is ML Experiment Tracking Important?

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Smart Data Collective

OCTOBER 20, 2021

As per the TDWI survey, more than a third (nearly 37%) of people has shown dissatisfaction with their ability to access and integrate complex data streams. Why is Data Integration a Challenge for Enterprises? As complexities in big data increase each day, data integration is becoming a challenge.

ML 133

ML ML Big Data Big Data 133

AWS Machine Learning Blog

SEPTEMBER 18, 2024

In addition to its groundbreaking AI innovations, Zeta Global has harnessed Amazon Elastic Container Service (Amazon ECS) with AWS Fargate to deploy a multitude of smaller models efficiently. Zeta’s AI innovation is powered by a proprietary machine learning operations (MLOps) system, developed in-house.

AWS 123

AWS Machine Learning Machine Learning ML 123