KDnuggets

JUNE 18, 2024

Going beyond technical skills; learn how to make a data science profile that stands out and helps you land your dream role.

Data Science 132

Data Science Data 132

Snowflake

JUNE 18, 2024

Welcome to Snowflake’s Startup Spotlight, where we ask startups about the problems they’re solving, the apps they’re building and the lessons they’ve learned during their startup journey. In this edition, we’ll learn why Terence Bennett, CEO of DreamFactory , and his team are championing a new way to think about API integrations. What was the genesis of DreamFactory?

Datasets 105

Datasets Programming Accessible Accessibility 105

KDnuggets

JUNE 18, 2024

Get your own local RAG system up and running in an embarrassingly few lines of code thanks to these 3 Llamas.

Coding 130

Coding Systems 130

Cloudera

JUNE 18, 2024

The world of Generative AI (GenAI) is rapidly evolving, with a wide array of models available for businesses to leverage. These models can be broadly categorized into two types: closed-source (proprietary) and open-source models. Closed-source models, such as OpenAI’s GPT-4o, Anthropic’s Claude 3, or Google’s Gemini 1.5 Pro, are developed and maintained by private and public companies.

Google Cloud 64

Google Cloud Government Cloud Architecture 64

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In Airflow, DAGs (your data pipelines) support nearly every use case. As these workflows grow in complexity and scale, efficiently identifying and resolving issues becomes a critical skill for every data engineer. This is a comprehensive guide with best practices and examples to debugging Airflow DAGs. You’ll learn how to: Create a standardized process for debugging to quickly diagnose errors in your DAGs Identify common issues with DAGs, tasks, and connections Distinguish between Airflow-relate

Data Pipeline

RandomTrees

JUNE 18, 2024

Customer services are continuously changing significantly. Now, it is not about waiting for hours plus and getting irritating phone menus. For instance, artificial intelligence (AI) chatbots powered by the latest machine learning and natural language processing (NLP) applications have redefined interaction between companies and their customers. The old days, where virtual assistants used to handle simple queries, are gone.

Machine Learning 52

Machine Learning Programming Algorithm Data Collection 52

phData: Data Engineering

JUNE 18, 2024

Machine learning (ML) is only possible because of all the data we collect. However, with data coming from so many different sources, it doesn’t always come in a format that’s easy for ML models to understand. Before you can take advantage of everything ML offers, much prep work is involved. In this blog, we’ll explain why you should prepare your data before use in machine learning , how to clean and preprocess the data, and a few tips and tricks about data preparation.

Machine Learning 52

Machine Learning Algorithm Data Preparation Data Warehouse 52