Pinterest Engineering

SEPTEMBER 20, 2023

Our commitment is evidenced by our history of building products that champion inclusivity. We know from experience that building for marginalized communities helps make the product work better for everyone. Signal Development and Indexing The process of developing our visual body type signal essentially begins with data collection.

Building 85

Building Pipeline-centric Machine Learning Datasets 85

Rockset

MARCH 18, 2024

Flink is one of the most popular stream processing technologies, ranked as a top five Apache project and backed by a diverse committer community including Alibaba and Apple. It powers steam processing at many companies including Uber, Netflix, and Linkedin. What are the challenges building RAG pipelines?

Cloud 64

Cloud Building Metadata Kafka 64

LinkedIn Engineering

DECEMBER 20, 2023

At LinkedIn, trust is the cornerstone for building meaningful connections and professional relationships. In this blog post, we discuss how we are harnessing AI to help us with abuse prevention and share an overview of our infrastructure and the role it plays in identifying and mitigating abusive behavior on our platform.

Building 91

Building Algorithm Kafka Machine Learning 91

LinkedIn Engineering

JANUARY 19, 2024

This multi-entity handover process involves huge amounts of data updating and cloning. Data consistency, feature reliability, processing scalability, and end-to-end observability are key drivers to ensuring business as usual (zero disruptions) and a cohesive customer experience. Push for eventual success of the request.

Recruitment 88

Recruitment Data Process Process Kafka 88

DareData

DECEMBER 16, 2023

In my last blog post, we’ve learned how to work with PyTorch tensors , the most important object in the PyTorch library. Tensors are the backbone of deep learning models so naturally we can use them to fit simpler machine learning models to our datasets. Let’s start! of the song Song loudness Song tempo. import torch.nn

Building 52

Building Deep Learning Datasets Coding 52

LinkedIn Engineering

MARCH 23, 2023

Co-Authors: Yuhong Cheng , Shangjin Zhang , Xinyu Liu, and Yi Pan Efficient data processing is crucial in reducing learning curves, simplifying maintenance efforts, and decreasing operational complexity. By unifying these pipelines, we have saved 94% of processing time. Samza , Spark and Apache Flink ).

Process 97

Process Lambda Architecture Kafka Datasets 97

Databand.ai

AUGUST 30, 2023

Building a Winning Data Quality Strategy: Step by Step Eitan Chazbani August 30, 2023 What Is a Data Quality Strategy? A data quality strategy details the processes, tools, and techniques employed to ensure your company’s data is accurate, consistent, complete, and up-to-date.

Building 52

Building Data Cleanse Data Governance Datasets 52

Lyft Engineering

APRIL 21, 2023

Building a large scale unsupervised model anomaly detection system — Part 1 Distributed Profiling of Model Inference Logs By Anindya Saha , Han Wang , Rajeev Prabhakar Introduction LyftLearn is Lyft’s ML Platform. In a previous blog post , we explored the architecture and challenges of the platform.

Systems 105

Systems Building Machine Learning Datasets 105

Christophe Blefari

APRIL 19, 2024

It was trained on a large dataset containing 15T tokens (compared to 2T for Llama 2). This is super interesting because it details important steps of the generative process. This blog shows how you can use Gen AI to evaluate inputs like translations with added reasons. — A great blog to answer a great question.

MySQL 130

MySQL Data Datasets SQL 130

Knowledge Hut

JULY 28, 2023

Data Labeling is the process of assigning meaningful tags or annotations to raw data, typically in the form of text, images, audio, or video. If some terminologies in the blog around Machine Learning seems unfamiliar to you, don’t worry we have the Best Data Science courses to help you out. How Does Data Labeling Work?

Machine Learning 52

Machine Learning Process Datasets Raw Data 52

AltexSoft

AUGUST 25, 2021

And this technology of Natural Language Processing is available to all businesses. Available methods for text processing and which one to choose. Tools you can use to build NLP models. What is Natural Language Processing? Here are some big text processing types and how they can be applied in real life.

Process 139

Process Deep Learning Datasets Machine Learning 139

Snowflake

APRIL 20, 2023

Snowflake users are already taking advantage of LLMs to build really cool apps with integrations to web-hosted LLM APIs using external functions , and using Streamlit as an interactive front end for LLM-powered apps such as AI plagiarism detection , AI assistant , and MathGPT. Join us in Vegas at our Summit to learn more.

Building 115

Building Unstructured Data Government Coding 115

Striim

NOVEMBER 17, 2023

Real-time data processing in the world of machine learning allows data scientists and engineers to focus on model development and monitoring. In this blog, we will focus on a PostgreSQL database. Specifically, in this dataset, 1 represents “setosa,” 2 represents “versicolor,” and 3 represents “virginica.”

Machine Learning 52

Machine Learning Data Process PostgreSQL Process 52

DataKitchen

DECEMBER 9, 2022

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. It involves bringing together people, processes, and technology to enable data-driven decision making and improve the efficiency of data-related workflows.

Machine Learning 52

Machine Learning Data Preparation Government Data Analytics 52

Tweag

APRIL 19, 2023

To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale. A single hospital makes many captures a day, producing terabytes of such data to store and process. Much like Spark, its module ray.data is focused on loading and processing data as scale. width , spec.

Medical 60

Medical Process Cloud Bytes 60

Knowledge Hut

FEBRUARY 29, 2024

Learning Data Analysis in Excel Data analysis is a process of inspecting, cleaning, transforming and modelling data with an objective of uncover the useful knowledge, results and supporting decision. Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered.

Data Science 98

Data Science Datasets Database Design Machine Learning 98

DataKitchen

MAY 10, 2024

This blog post introduces five critical use cases for data observability, each pivotal in maintaining the integrity and usability of data throughout its journey in any enterprise. Data ingestion observability helps organizations avoid costly mistakes when poor-quality data is used in critical business processes.

Data Ingestion 56

Data Ingestion Datasets Data Coding 56

Christophe Blefari

MARCH 22, 2024

Usually all the process takes me a whole Friday. The process works well, but as you can see, because I use fresh news, it's just-in-time. ❤️ I rarely say it, if Data News helps you save time you should consider taking a paid subscription (60€/year) to help me covers the blog fees and my writing Fridays.

Electronics 130

Electronics Media Data Python 130

DareData

JULY 5, 2023

Learn how we build data lake infrastructures and help organizations all around the world achieving their data goals. In today's data-driven world, organizations are faced with the challenge of managing and processing large volumes of data efficiently. This flexibility can lead to valuable insights and opportunities for business growth.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

ProjectPro

JANUARY 15, 2021

And honestly, there are a lot of real-world machine learning datasets around you that you can opt to start practicing your fundamental data science and machine learning skills, even without having to complete a comprehensive data science or machine learning course. Table of Contents What is a dataset in machine learning?

Datasets 40

Datasets Machine Learning Retail Banking 40

ProjectPro

FEBRUARY 16, 2023

Whether you’re looking to track objects in a video stream, build a face recognition system, or edit images creatively, OpenCV Python implementation is the go-to choice for the job. At the core of such applications lies the science of machine learning, image processing, computer vision, and deep learning. What is OpenCV Python?

Python 98

Python Process Deep Learning Algorithm 98

Netflix Tech

OCTOBER 19, 2020

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices.

Building 93

Building Transportation Metadata Java 93

ProjectPro

JANUARY 25, 2022

Here’s What You Need to Know About PySpark This blog will take you through the basics of PySpark, the PySpark architecture, and a few popular PySpark libraries , among other things. Furthermore, PySpark allows you to interact with Resilient Distributed Datasets (RDDs) in Apache Spark and Python. Why use PySpark?

Big Data 52

Big Data Data Process Process Kafka 52

Workfall

JULY 18, 2023

As one of those wizards, we’ve seen the challenges we face: the struggle to transform massive datasets into meaningful insights, all while keeping queries fast and our system scalable. But then, a game-changer emerged – DBT (Data Build Tool). In this blog, we will cover: What is DBT? DBT , the Data Build Tool.

Datasets 77

Datasets Entertainment Data Workflow Data 77

DareData

FEBRUARY 28, 2024

In this post of the PyTorch Introduction, we’ll learn how to use custom datasets with PyTorch, particularly tabular, vision and text data PyTorch is one of the hottest libraries in the Deep Learning field right now. We’ve used few custom datasets in our examples and previous blog posts. Let’s start!

Datasets 52

Datasets Deep Learning Data Machine Learning 52

Maxime Beauchemin

JANUARY 7, 2018

Batch data processing  — historically known as ETL —  is extremely challenging. In this post, we’ll explore how applying the functional programming paradigm to data engineering can bring a lot of clarity to the process. The greater the claim made using analytics, the greater the scrutiny on the process should be.

Data Engineering 183

Data Engineering Data Engineer Data Process Process 183

DataKitchen

MAY 10, 2024

Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset.

Raw Data 49

Raw Data Data Ingestion Datasets Data 49

DataKitchen

MAY 10, 2024

This blog post delves into the third critical use case for Data Observation and Data Quality Validation: development and Deployment. The Fourth of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production.

Data Ingestion 49

Data Ingestion Datasets Coding Data 49

Pinterest Engineering

JULY 25, 2023

Each dataset needs to be securely stored with minimal access granted to ensure they are used appropriately and can easily be located and disposed of when necessary. As businesses grow, so does the variety of these datasets and the complexity of their handling requirements.

Big Data 95

Big Data Accessible Accessibility Hadoop 95

LinkedIn Engineering

DECEMBER 20, 2023

This blog post delves into the AutoML framework for LinkedIn’s content abuse detection platform and its role in improving and fortifying content moderation systems at LinkedIn. Automated Machine Learning (AutoML) refers to a framework or platform that automates the entire machine learning process. What is AutoML?

Machine Learning 90

Machine Learning Datasets Algorithm Architecture 90

ProjectPro

DECEMBER 7, 2021

Table of Contents The Ultimate Guide to Build a Data Analyst Portfolio Data Analyst Portfolio Platforms Skills to Showcase On Your Data Analyst Portfolio What to Include in Your Data Analyst Portfolio? So, let us begin the journey towards building the perfect portfolio as a Data Analyst!! 2) The skill to clean datasets completely.

Portfolio 52

Portfolio Building Data Mining Data Analysis 52

DareData

JANUARY 13, 2024

Continuing the Pytorch series, in this post we’ll learn about how non-linearities help solve complex problems in the context of neural networks In the last blog posts of the PyTorch Introduction series, we spoke about introduction to tensor objects and building a simple linear model using PyTorch. Let’s start!

Datasets 52

Datasets Deep Learning Architecture Algorithm 52

Cloudera

DECEMBER 13, 2023

He is responsible for introducing common tools and processes and enhancing the technology, tools and data available to employees, empowering them to use their expertise and creativity in innovative ways. The post Cloudera Customer Story appeared first on Cloudera Blog. Please read the full story here.

Pipeline-centric 88

Pipeline-centric Professional Services Datasets BI 88

Data Engineering Weekly

DECEMBER 10, 2023

Capital One: Insights on building a data strategy to drive business value One of the hotly debated and many companies struggling with is to build an agile data strategy to drive business value. I can’t wait for someone to build a bot around it!!! Tuning hyperparameters like rank and dataset diversity is key.

Data Engineering 76

Data Engineering Data Engineer Engineering Metadata 76

Snowflake

APRIL 24, 2024

Building top-tier enterprise-grade intelligence using LLMs has traditionally been prohibitively expensive and resource-hungry, and often costs tens to hundreds of millions of dollars. Enterprises want to use LLMs to build conversational SQL data copilots, code copilots and RAG chatbots.

Amazon Web Services 145

Amazon Web Services SQL AWS Architecture 145

Data Engineering Weekly

MARCH 10, 2024

Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects. Thanks to Ideas2IT Technologies for hosting us in their fantastic space.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

RandomTrees

FEBRUARY 2, 2024

Cohere Cohere helps in building powerful and secure applications and helps in searching, understanding, and translating text. It creates summaries of large datasets and identifies anomalies in data. It allows users to access its language model and powerful functions to build various application scenarios.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52