KDnuggets

JULY 7, 2025

DuckDB is an SQL database that you can run right in your notebook. Unlike other SQL databases, you don’t need to configure the server. We also did this using a real-life data project that Uber requested in the data scientist recruitment process. Nate Rosidi is a data scientist and in product strategy.

SQL 265

SQL Data Science Natural Language Processing Machine Learning 265

KDnuggets

JULY 24, 2025

Data from external sources: Web scraping, Google Sheets, Excel, and SQLite databases. Learn Python Platform: Kaggle Level: Beginner to intermediate Why Take It: Short interactive lessons with real-world data. Databases and backend integration: Interact with MySQL and MongoDB using Python.

Python 252

Python Data Science Natural Language Processing Machine Learning 252

Flipboard

JULY 1, 2025

This is a must-have bookmark for any data scientist working with Python, encompassing everything from data analysis and machine learning to web development and automation. Ideal for data scientists and engineers working with databases and complex data models.

Data Science 169

Data Science Natural Language Processing Machine Learning Machine Learning 169

KDnuggets

JUNE 23, 2025

It supports data scientists and engineers working together. mlruns This command uses an SQLite database for metadata storage and saves artifacts in the mlruns directory. It manages the entire machine learning lifecycle. It provides tools to simplify workflows. These tools help develop, deploy, and maintain models.

Machine Learning 228

Machine Learning Machine Learning Natural Language Processing Data Science 228

KDnuggets

JULY 16, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Next post => Latest Posts The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs 10 Python Math & Statistical Analysis One-Liners 10 GitHub Repositories for Python Projects Building End-to-End Data Pipelines: From Data Ingestion to Analysis (..)

Python 282

Python Natural Language Processing Data Science Machine Learning 282

KDnuggets

JULY 17, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Latest Posts Build Your Own Simple Data Pipeline with Python and Docker 10 Surprising Things You Can Do with Python’s collections Module The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs 10 Python Math & Statistical Analysis (..)

Data Pipeline 276

Data Pipeline Python ETL Natural Language Processing 276

Flipboard

JUNE 27, 2025

His company’s tool, a relational foundation model (RFM), is a new kind of pre-trained AI that brings the “zero-shot” capabilities of large language models (LLMs) to structured databases. Expensive and time-consuming bottlenecks prevent most organizations from being truly agile with their data.

Database 165

Database Deep Learning Deep Learning ML 165

Dataconomy

JUNE 23, 2025

Definition and purpose of a data set The core purpose of a data set is to provide a clear, organized method for storing data that can be easily accessed and analyzed. For example, a sales data set can reveal trends in customer purchases over time, informing marketing strategies.

Database 91

Database Machine Learning Machine Learning Analytics 91

KDnuggets

JULY 22, 2025

API, Database, Campaign, Analytics, Frontend, Testing, Outreach, CRM] # Conclusion These Python one-liners show how useful Python is for JSON data manipulation. This one-liner extracts and combines elements from nested lists, creating a single flat structure thats easier to work with in subsequent operations.

Python 187

Python Natural Language Processing Data Science Machine Learning 187

ODSC - Open Data Science

JULY 11, 2025

From Moneyball’s transformative impact on baseball to real-time player tracking in basketball and football, data-driven decision-making is redefining how games are played, coached, and consumed. Sports data offers several benefits for learning and experimentation. It’s relatable — many data scientists are already passionate fans.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

Dataconomy

JULY 7, 2025

Cloud-based implementations The adoption of cloud storage solutions is becoming increasingly common for data lakes. Furthermore, NoSQL databases serve as effective platforms for implementing data lakes, allowing for rapid ingestion and retrieval of diverse data types.

Data Lakes 91

Data Lakes Data Warehouse Hadoop Analytics 91

KDnuggets

JUNE 24, 2025

Step 5: Initialize the Database Run the following commands to set up the database: chmod +x start-database.sh./start-database.sh The Postgres database will start in a container at localhost:5432. Python for Modern Data Workflows: Need Help Deciding? You will see the following message at the server side. start-database.sh

Natural Language Processing 173

Natural Language Processing Data Science Machine Learning Machine Learning 173

KDnuggets

JUNE 6, 2025

Context Manager Pattern for Resource Management When working with resources like files, database connections, or network sockets, you need to ensure they’re properly opened and closed, even if an error occurs. Example: Suppose you’re fetching user data from a database and want to provide context when a database error occurs.

Python 225

Python Natural Language Processing Data Science Machine Learning 225

KDnuggets

JULY 23, 2025

You can also use a backend database such as SQLite or PostgreSQL to store its state. For data privacy, you are responsible for your own privacy as a user deploying LiteLLM yourself, but this approach is more secure since the data never leaves your controlled environment except when sent to the LLM providers.

Natural Language Processing 193

Natural Language Processing Data Science Python Machine Learning 193

Flipboard

JUNE 17, 2025

Organizations manage extensive structured data in databases and data warehouses. Large language models (LLMs) have transformed natural language processing (NLP), yet converting conversational queries into structured data analysis remains complex. This setup uses automatic mounting of the Data Catalog in Amazon Redshift.

AWS 147

AWS SQL Database Natural Language Processing 147

Dataconomy

JUNE 12, 2025

Data engineers are the unsung heroes of the data-driven world, laying the essential groundwork that allows organizations to leverage their data for enhanced decision-making and strategic insights. Their role has grown increasingly critical as businesses rely on large volumes of data to inform their operations and strategies.

Data Engineering 91

Data Engineering Data Engineer Data Engineering Data Engineering 91

KDnuggets

JULY 17, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Latest Posts 10 Surprising Things You Can Do with Python’s collections Module The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs 10 Python Math & Statistical Analysis One-Liners 10 GitHub Repositories for Python Projects Building (..)

Natural Language Processing 298

Natural Language Processing Data Science Python Machine Learning 298

How to Learn Machine Learning

APRIL 26, 2025

The field of data science is now one of the most preferred and lucrative career options available in the area of data because of the increasing dependence on data for decision-making in businesses, which makes the demand for data science hires peak. Data Sources and Collection Everything in data science begins with data.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

Flipboard

JULY 18, 2025

But as data scales, the benefits become more noticeable, especially in memory-bound or performance-critical applications. Nate Rosidi is a data scientist and in product strategy. Nate writes on the latest trends in the career market, gives interview advice, shares data science projects, and covers everything SQL.

Data Science 150

Data Science Natural Language Processing Python Machine Learning 150

Data Science Dojo

JULY 1, 2025

Whether you’re a data scientist, AI engineer, or business leader, understanding these protocols is essential for building the next generation of intelligent systems. What Are Agentic AI Communication Protocols?

AI 195

AI AI Data Scientist Database 195

Flipboard

JULY 16, 2025

By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Latest Posts The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs 10 Python Math & Statistical Analysis One-Liners 10 GitHub Repositories for Python Projects Building End-to-End Data Pipelines: From Data Ingestion to Analysis Bootstrapping (..)

Machine Learning 149

Machine Learning Machine Learning Natural Language Processing Data Science 149

KDnuggets

JUNE 11, 2025

MCP servers are lightweight programs or APIs that expose real-world tools like databases, file systems, or web services to AI models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. So, what exactly is an MCP server and client?

Natural Language Processing 181

Natural Language Processing Data Science Machine Learning Machine Learning 181

AWS Machine Learning Blog

DECEMBER 4, 2024

Its sales analysts face a daily challenge: they need to make data-driven decisions but are overwhelmed by the volume of available information. They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels.

AWS 106

AWS AI AI SQL 106

Dataconomy

JANUARY 15, 2025

If your database administrator has the utmost confidence in the data engineer and vice versa due to their continuous professional growth, then team members will be apt to interact and work more closely together. Who is responsible for data analyzing? Which team member oversees data warehousing?

Database Administration 91

Database Administration Database Data Analyst Data Scientist 91

AWS Machine Learning Blog

OCTOBER 23, 2024

Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments. The resulting vectors are stored in OpenSearch Service databases for efficient retrieval and querying. Not anymore!

AI 89

AI AI AWS Database 89

AWS Machine Learning Blog

OCTOBER 29, 2024

Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. Chris Pecora is a Generative AI Data Scientist at Amazon Web Services.

AWS 113

AWS AI AI Data Scientist 113

AWS Machine Learning Blog

OCTOBER 24, 2024

It allows data scientists and machine learning engineers to interact with their data and models and to visualize and share their work with others with just a few clicks. SageMaker Canvas has also integrated with Data Wrangler , which helps with creating data flows and preparing and analyzing your data.

Data Warehouse 113

Data Warehouse Machine Learning Machine Learning Cloud Data 113

Data Science Dojo

JANUARY 22, 2025

To get you started, Data Science Dojo and Weaviate have teamed up to bring you an exciting webinar series: Master Vector Embeddings with Weaviate. We have carefully curated the series to empower AI enthusiasts, data scientists, and industry professionals with a deep understanding of vector embeddings.

Database 195

Database ML ML AI 195

Flipboard

JUNE 27, 2025

It requires building pipelines that bring in context from user history, prior interactions, tool calls, and internal databases — all in a format that’s easily digestible by a Transformer-based system. Context engineering doesn’t just mean “adding more stuff” to your prompt. billion across its Mumbai and Hyderabad regions, contributing $23.3

AWS 151

AWS AI AI Database 151

KDnuggets

JUNE 25, 2025

The interface provides transparent, citation-backed answers, “every answer is backed by verifiable citations from our comprehensive database” so you can trust the information. Other tools include customizable dashboards to track topics, and a “Reference Check” feature to optimize your own manuscript’s bibliography.

Natural Language Processing 224

Natural Language Processing Data Science AI AI 224

Pickl AI

DECEMBER 26, 2024

Defining Cloud Computing in Data Science Cloud computing provides on-demand access to computing resources such as servers, storage, databases, and software over the Internet. For Data Science, it means deploying Analytics , Machine Learning , and Big Data solutions on cloud platforms without requiring extensive physical infrastructure.

Data Science 52

Data Science Cloud Computing Machine Learning Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 26, 2024

A semantic cache system operates at its core as a database storing numerical vector embeddings of text queries. With OpenSearch Serverless, you can establish a vector database suitable for setting up a robust cache system. The new generation is then sent to the client and used to update the vector database.

AWS 123

AWS Machine Learning Machine Learning AI 123

KDnuggets

JUNE 12, 2025

Because it’s modular, you can easily extend it, maybe add a search bar using Streamlit, store chunks in a vector database like FAISS for smarter lookups, or even plug this into a chatbot. Examples of Articles Conclusion In this guide, you’ve learned how to build a flexible and powerful PDF processing pipeline using only open-source tools.

Data Science 250

Data Science Natural Language Processing Python Machine Learning 250

AWS Machine Learning Blog

NOVEMBER 26, 2024

Customers are looking for success stories about how best to adopt the culture and new operational solutions to support their data scientists. Datadog is a monitoring service for cloud-scale applications, bringing together data from servers, databases, tools and services to present a unified view of your entire stack.

AWS 117

AWS ML ML Data Scientist 117

phData

NOVEMBER 4, 2024

By providing an integrated environment for data preparation, machine learning, and collaborative analytics, Dataiku empowers teams to harness the full potential of their data without requiring extensive technical expertise. The platform allows data scientists, analysts, and business stakeholders to work together seamlessly.

Machine Learning 69

Machine Learning Machine Learning Data Science Data Preparation 69

Pickl AI

JUNE 29, 2025

This is where the concept of a data lake comes in. In this comprehensive blog, we will explore what a data lake is , its core components, how it compares to other data storage solutions like data warehouses and databases, its value, challenges, and deployment in the cloud. Why Do You Need a Data Lake?

Data Lakes 52

Data Lakes Data Warehouse Azure Data Silos 52

AWS Machine Learning Blog

NOVEMBER 15, 2024

Multimodal Retrieval Augmented Generation (MM-RAG) is emerging as a powerful evolution of traditional RAG systems, addressing limitations and expanding capabilities across diverse data types. Traditionally, RAG systems were text-centric, retrieving information from large text databases to provide relevant context for language models.

AWS 109

AWS Computer Science Computer Science Database 109