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

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

AWS Machine Learning Blog

AUGUST 30, 2024

In this post, we provide an overview of the Meta Llama 3 models available on AWS at the time of writing, and share best practices on developing Text-to-SQL use cases using Meta Llama 3 models. Meta Llama 3’s capabilities enhance accuracy and efficiency in understanding and generating SQL queries from natural language inputs.

SQL 93

SQL AWS Database AI 93

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

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

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

Data Science Dojo

JULY 6, 2023

It integrates well with other Google Cloud services and supports advanced analytics and machine learning features. It provides a scalable and fault-tolerant ecosystem for big data processing. Spark offers a rich set of libraries for data processing, machine learning, graph processing, and stream processing.

Data Engineering 195

Data Engineering Data Engineering Data Engineer Data Engineering 195

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 125

AWS Machine Learning Machine Learning ML 125

Smart Data Collective

JUNE 4, 2021

Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating data models. These data models predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.

Data Science 133

Data Science Exploratory Data Analysis Machine Learning Machine Learning 133

AWS Machine Learning Blog

FEBRUARY 21, 2025

Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks. Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL.

Data Science 83

Data Science AWS Hadoop Data Scientist 83

Dataconomy

AUGUST 7, 2023

AI databases are specialized to store, manage, and retrieve data for artificial intelligence and machine learning applications ( Image credit ) What is an AI database? These formats play a significant role in how data is processed, analyzed, and used to develop AI models.

Database 168

Database AI AI ML 168

phData

OCTOBER 27, 2023

The term “feature store” is often used when architecting the ideal Machine Learning platform. Using dbt to transform data into features allows engineers to take advantage of the expressibility of SQL without worrying about data lineage.

Machine Learning 52

Machine Learning Machine Learning Python ML 52

The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. An integrated model factory to develop, deploy, and monitor models in one place using your preferred tools and languages.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

MARCH 30, 2023

Using Azure ML to Train a Serengeti Data Model, Fast Option Pricing with DL, and How To Connect a GPU to a Container Using Azure ML to Train a Serengeti Data Model for Animal Identification In this article, we will cover how you can train a model using Notebooks in Azure Machine Learning Studio.

Azure 52

Azure ML ML Data Models 52

phData

SEPTEMBER 19, 2023

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. Data modeling plays a pivotal role in defining the structure, relationships, and semantics of data within a data lake. Consistency of data throughout the data lake.

Data Lakes 52

Data Lakes Data Models Data Modeling Data Warehouse 52

Towards AI

JANUARY 11, 2024

Tabular data is the data in the typical table — some columns and rows are structured well, like in Excel or SQL data. It's the most common usage of data forms in many data use cases. With the power of LLM, we would learn how to explore the data and perform data modeling.

Python 126

Python Clean Data Data Science SQL 126

Smart Data Collective

MAY 29, 2023

With these changes comes the challenge of understanding how to gather, manage, and make sense of the data collected in various markets. With the introduction and use of machine learning, AI tech is enabling greater efficiencies with respect to data and the insights embedded in the information.

Data Analyst 104

Data Analyst SQL Data Scientist Data Analysis 104

Towards AI

APRIL 22, 2024

To create, update, and manage a relational database, we use a relational database management system that most commonly runs on Structured Query Language (SQL). NoSQL databases — NoSQL is a vast category that includes all databases that do not use SQL as their primary data access language.

Database 102

Database SQL Clustering Big Data 102

IBM Journey to AI blog

SEPTEMBER 19, 2023

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

DagsHub

OCTOBER 23, 2024

Unstructured data makes up 80% of the world's data and is growing. Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Tableau

OCTOBER 8, 2021

Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey. With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics.

Tableau 138

Tableau Analytics Analytics Machine Learning 138

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Role of Data Scientists Data Scientists are the architects of data analysis.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Tableau

DECEMBER 1, 2021

Query allowed customers from a broad range of industries to connect to clean useful data found in SQL and Cube databases. The prototype could connect to multiple data sources at the same time—a precursor to Tableau’s investments in data federation. Relationships in Tableau 2020.2 (May Beginning in Tableau 2020.2,

Tableau 145

Tableau ML ML Database 145

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

AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. Data professionals such as data scientists want to use the power of Apache Spark , Hive , and Presto running on Amazon EMR for fast data preparation; however, the learning curve is steep.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

AWS Machine Learning Blog

MAY 31, 2024

Select the uploaded file and from Actions dropdown and choose the Query with S3 Select option to query the.csv data using SQL if the data was loaded correctly. In this demonstration, let’s assume that you need to remove the data related to a particular customer. He is passionate about cloud and machine learning.

AWS 133

AWS Machine Learning Machine Learning Database 133

Pickl AI

OCTOBER 16, 2024

Summary: Relational Database Management Systems (RDBMS) are the backbone of structured data management, organising information in tables and ensuring data integrity. This article explores RDBMS’s features, advantages, applications across industries, the role of SQL, and emerging trends shaping the future of data management.

Database 52

Database SQL Analytics Analytics 52

phData

SEPTEMBER 26, 2023

Data flows from the current data platform to the destination. The necessary access is granted so data flows without issue. SQL Server Agent jobs). Either way, it’s important to understand what data is transformed, and how so. Reporting The goal of this exercise is to determine how data is consumed.

SQL 52

SQL Database ETL Data Models 52

Dataconomy

OCTOBER 15, 2024

Gen AI can automate microservice generation within a low-code platform by interpreting user-defined requirements and generating service interfaces, data models, and even testing scripts. Data integration and workflow automation The highest pain points in the application development would be through data integration.

AI 127

AI AI Database Natural Language Processing 127

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. Its PostgreSQL foundation ensures compatibility with most SQL clients. Strengths : Real-time analytics, built-in machine learning capabilities, and fast querying with standard SQL.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Pickl AI

JANUARY 9, 2025

Key Takeaways Operations Analysts optimise efficiency through data-driven decision-making. Expertise in tools like Power BI, SQL, and Python is crucial. Expertise in programs like Microsoft Excel, SQL , and business intelligence (BI) tools like Power BI or Tableau allows analysts to process and visualise data efficiently.

Power BI 52

Power BI Machine Learning Machine Learning Tableau 52

Tableau

OCTOBER 8, 2021

Leveraging Looker’s semantic layer will provide Tableau customers with trusted, governed data at every stage of their analytics journey. With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics.

Tableau 98

Tableau Analytics Analytics Machine Learning 98

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 Engineer Data Engineering 52

Pickl AI

MAY 30, 2023

Top Job-Oriented Courses for Higher Salaries: Business Analytics Certification Program The program has been specifically designed for the aspirants interested in Analytics which includes to expand their skills in Statistics, Predictive Modelling and Machine Learning. Through the Data Science Job Guarantee Program by Pickl.AI

Data Science 52

Data Science Data Mining Data Mining Data Mining 52

Towards AI

JULY 17, 2024

My approach to graph-based Retrieval Augmented Generation The approach is a bit more rooted in traditional methods, I parse the Data Model (an SQL-based relational system) into Nodes and Relationships in a graph database and then provide an endpoint where those relationships can be queried to provide a source of truth.

Database 110

Database Data Models Data Modeling SQL 110

Women in Big Data

DECEMBER 9, 2024

Lookers strength lies in its ability to connect to a wide variety of data sources. Examples include SQl, DWH, and Cloud based systems (Google Bigquery). With Looker, you can share dashboards and visualizations seamlessly across teams, providing stakeholders with access to real-time data.

Big Data 52

Big Data Big Data Power BI Tableau 52

ODSC - Open Data Science

APRIL 26, 2023

For example, a data scientist would be a good fit for a team that is in charge of handling large swaths of data and creating actionable insights from them. In another industry what matters is being able to predict behaviors in the medium and short terms, and this is where a machine learning engineer might come to play.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52