June, 2023

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Generative AI and the Future of Data Engineering

Monte Carlo

Generative AI is taking the world by storm – here’s what it means for data engineering and why data observability is critical for this groundbreaking technology to succeed. Maybe you’ve noticed the world has dumped the internet, mobile, social, cloud and even crypto in favor of an obsession with generative AI. But is there more to generative AI than a fancy demo on Twitter?

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Modern Data Engineering with MAGE: Empowering Efficient Data Processing

Analytics Vidhya

Introduction In today’s data-driven world, organizations across industries are dealing with massive volumes of data, complex pipelines, and the need for efficient data processing. Traditional data engineering solutions, such as Apache Airflow, have played an important role in orchestrating and controlling data operations in order to tackle these difficulties.

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Google Domains to shut down

The Pragmatic Engineer

👋 Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. In every issue, I cover topics related to Big Tech and startups through the lens of engineering managers and senior engineers. In this article, we cover one out of five topics from today’s subscriber-only The Scoop issue. To get full issues twice a week, subscribe here.

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A Comprehensive Guide to Convolutional Neural Networks

KDnuggets

Artificial Intelligence has been witnessing monumental growth in bridging the gap between the capabilities of humans and machines. Researchers and enthusiasts alike, work on numerous aspects of the field to make amazing things happen. One of many such areas is the domain of Computer Vision.

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Generative AI Deep Dive: Advancing from Proof of Concept to Production

Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage

Executive leaders and board members are pushing their teams to adopt Generative AI to gain a competitive edge, save money, and otherwise take advantage of the promise of this new era of artificial intelligence. There's no question that it is challenging to figure out where to focus and how to advance when it’s a new field that is evolving everyday. 💡 This new webinar featuring Maher Hanafi, VP of Engineering at Betterworks, will explore a practical framework to transform Generative AI pr

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Ensuring the Successful Launch of Ads on Netflix

Netflix Tech

By Jose Fernandez , Ed Barker , Hank Jacobs Introduction In November 2022, we introduced a brand new tier —  Basic with ads. This tier extended existing infrastructure by adding new backend components and a new remote call to our ads partner on the playback path. As we were gearing up for launch, we wanted to ensure it would go as smoothly as possible.

Algorithm 139
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GPT-4 + Streaming Data = Real-Time Generative AI

Confluent

ChatGPT and data streaming can work together for any company. Learn a basic framework for using GPT-4 and streaming to build a real-world production application.

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More Trending

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The Journey of a Senior Data Scientist and Machine Learning Engineer at Spice Money

Analytics Vidhya

Introduction Meet Tajinder, a seasoned Senior Data Scientist and ML Engineer who has excelled in the rapidly evolving field of data science. Tajinder’s passion for unraveling hidden patterns in complex datasets has driven impactful outcomes, transforming raw data into actionable intelligence. In this article, we explore Tajinder’s inspiring success story.

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An explosion in software engineers using AI coding tools?

The Pragmatic Engineer

GitHub surveyed 500 developers in the US for a sense of how they use AI coding tools. I examine the results and add context on how the survey was conducted.

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AI: Large Language & Visual Models

KDnuggets

This article discusses the significance of large language and visual models in AI, their capabilities, potential synergies, challenges such as data bias, ethical considerations, and their impact on the market, highlighting their potential for advancing the field of artificial intelligence.

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New Approaches For Detecting AI-Generated Profile Photos

LinkedIn Engineering

Co-authors: Shivansh Mundra , Gonzalo Aniano Porcile , Smit Marvaniya , Hany Farid A core part of what we do on the Trust Data Team at LinkedIn is create, deploy, and maintain models that detect and prevent many types of abuse. This spans the detection and prevention of fake accounts, account takeovers, and policy-violating content. We are constantly working to improve and increase the effectiveness of our anti-abuse defenses to protect the experiences of our members and customers.

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Leading the Development of Profitable and Sustainable Products

Speaker: Jason Tanner

While growth of software-enabled solutions generates momentum, growth alone is not enough to ensure sustainability. The probability of success dramatically improves with early planning for profitability. A sustainable business model contains a system of interrelated choices made not once but over time. Join this webinar for an iterative approach to ensuring solution, economic and relationship sustainability.

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Native Frame Rate Playback

Netflix Tech

by Akshay Garg , Roger Quero Introduction Maximizing immersion for our members is an important goal for the Netflix product and engineering teams to keep our members entertained and fully engaged in our content. Leveraging a good mix of mature and cutting-edge client device technologies to deliver a smooth playback experience with glitch-free in-app transitions is an important step towards achieving this goal.

Algorithm 124
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Building Real-time Machine Learning Foundations at Lyft

Lyft Engineering

Written by Konstantin Gizdarski and Martin Liu at Lyft. In early 2022, Lyft already had a comprehensive Machine Learning Platform called LyftLearn composed of model serving , training , CI/CD, feature serving , and model monitoring systems. On the real-time front, LyftLearn supported real-time inference and input feature validation. However, streaming data was not supported as a first-class citizen across many of the platform’s systems — such as training, complex monitoring, and others.

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What Data Engineers Really Do?

Analytics Vidhya

In a data-driven world, behind-the-scenes heroes like data engineers play a crucial role in ensuring smooth data flow. Imagine being an online shopper who suddenly receives irrelevant recommendations. A data engineer investigates the issue, identifies a glitch in the e-commerce platform’s data funnel, and swiftly implements seamless data pipelines.

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Should you optimize for all-cash compensation, if possible?

The Pragmatic Engineer

👋 Hi, this is Gergely with a bonus, free issue of the Pragmatic Engineer Newsletter. In every issue, I cover topics related to Big Tech and high-growth startups through the lens of engineering managers and senior engineers. In this article, we cover one out of four topics from today’s subscriber-only The Scoop issue. If you’re not a full subscriber yet, you missed this week’s deep-dive on Shopify’s leveling split.

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Navigating the Future: Generative AI, Application Analytics, and Data

Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

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5 Free Julia Books For Data Science

KDnuggets

Discover the full potential of the Julia programming language for data analysis and modeling with a comprehensive guide that covers everything from its syntax to advanced techniques.

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What Is an Event in the Apache Kafka Ecosystem?

Confluent

Get an introduction into the world of events and event-driven architecture in Apache Kafka. Learn what events are and the role they play in event design, event streaming, and event-driven design.

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Extending Databricks Unity Catalog with an Open Apache Hive Metastore API

databricks

Today, we are excited to announce the preview of a Hive Metastore (HMS) interface for Databricks Unity Catalog, which allows any software compatible.

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Lyft Expands Team to Czechia

Lyft Engineering

Introducing Lyft Engineering: Hello Czechia! Ahoj! Lyft is opening offices in Czechia ?? and hiring for full-time positions on end-to-end product, science, and engineering teams. We’re looking for driven engineers to fortify our European operations and solve some of the hardest problems in building large distributed systems to support rideshare, mapping, and more.

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Get Better Network Graphs & Save Analysts Time

Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

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Top 10 Powerful Data Modeling Tools to Know in 2023

Analytics Vidhya

Introduction In the era of data-driven decision-making, having accurate data modeling tools is essential for businesses aiming to stay competitive. As a new developer, a robust data modeling foundation is crucial for effectively working with databases. Properly configured data structures ensure a smoother workflow and prevent data loss or misplacement.

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Announcing halide-haskell - a Haskell interface for the Halide image and array processing language

Tweag

The availability of deep learning frameworks like PyTorch or JAX has revolutionized array processing, regardless of whether one is working on machine learning tasks or other numerical algorithms. The Haskell library ecosystem has been catching up as well, and there are now multiple good array libraries. However, writing high-performance array processing code in Haskell is still a non-trivial endeavor.

Process 101
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Your Ultimate Guide to Chat GPT and Other Abbreviations

KDnuggets

Everyone seems to have gone crazy about ChatGPT, which has become a cultural phenomenon. If you’re not on the ChatGPT train yet, this article might help you better understand the context and excitement around this innovation.

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Data Pipeline Observability: A Model For Data Engineers

Databand.ai

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. Specifically, observability provides insights into the pipeline’s internal states and how they interact with the system’s outputs. We believe the world’s data pipelines need better data observability.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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Pandas 2.0: A Game-Changer for Data Scientists?

Towards Data Science

The Top 5 Features for Efficient Data Manipulation This April, pandas 2.0.0 was officially launched , making huge waves across the data science community. Photo by Yancy Min on Unsplash. Due to its extensive functionality and versatility, pandas has secured a place in every data scientist’s heart. From data input/output to data cleaning and transformation, it’s nearly impossible to think about data manipulation without import pandas as pd, right ?

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Top Backend Project Ideas for Your Portfolio

Knowledge Hut

Having knowledge of real-world software applications or projects are very essential for any projects for backend developers aspiring software engineers or developers. The portfolio projects showcase their talents and skills whenever they try to look for new opportunities and jobs. This article is mainly focused on explaining different backend projects for beginners or students, intermediate learners, or those who have mid enough software development experience building large scalable projects.

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Mr. Pavan’s Data Engineering Journey Drives Business Success

Analytics Vidhya

Introduction We had an amazing opportunity to learn from Mr. Pavan. He is an experienced data engineer with a passion for problem-solving and a drive for continuous growth. Throughout the conversation, Mr. Pavan shares his journey, inspirations, challenges, and accomplishments. Thus, providing valuable insights into the field of data engineering. As we explore Mr.

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DataKitchen in DBTA 100 2023: The Companies That Matter Most in Data

DataKitchen

The need to balance data safety with new data initiatives, deliver business value, and change company culture around data tops this year's list of data and analytics management challenges. To help bring new resources and innovation to light, each year, Database Trends and Applications magazine presents the DBTA 100, a list of forward-thinking companies seeking to expand what's possible with data for their customers.

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How To Get Promoted In Product Management

Speaker: John Mansour

If you're looking to advance your career in product management, there are more options than just climbing the management ladder. Join our upcoming webinar to learn about highly rewarding career paths that don't involve management responsibilities. We'll cover both career tracks and provide tips on how to position yourself for success in the one that's right for you.

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10 ChatGPT Plugins for Data Science Cheat Sheet

KDnuggets

For an overview of what we believe to be the 10 of the best ChatGPT plugins for data science, check out our latest cheat sheet.

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Who Is Responsible For Data Quality? 5 Different Answers From Real Data Teams

Monte Carlo

Sure, data quality is everyones’ problem. But who is responsible for data quality? Given the variations in approach and mixed success, we have a lot of natural experiments from which to learn. Some organizations will attempt to diffuse the responsibility widely across data stewards, data owners, data engineering and governance committees, each owning a fraction of the data value chain.

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What Pride and allyship mean to me by Steve Foreshew-Cain

Scott Logic

Every year at this time, I like to share my thoughts on the continuing relevance of Pride Month; you can read my posts here from 2021 and 2022. This Pride Month, we’re going to share insights from the Scott Logic team on what Pride and allyship mean to them, and why they value working in an inclusive environment. I’ll get the ball rolling. What does Pride mean to you?

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Top 12 Data Engineering Project Ideas [With Source Code]

Knowledge Hut

Welcome to the world of data engineering, where the power of big data unfolds. If you're aspiring to be a data engineer and seeking to showcase your skills or gain hands-on experience, you've landed in the right spot. Get ready to delve into fascinating data engineering project concepts and explore a world of exciting data engineering projects in this article.

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How Embedded Analytics Gets You to Market Faster with a SAAS Offering

Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.