Snowflake
FEBRUARY 12, 2025
AI agents, autonomous systems that perform tasks using AI, can enhance business productivity by handling complex, multi-step operations in minutes. Agents need to access an organization's ever-growing structured and unstructured data to be effective and reliable. As data connections expand, managing access controls and efficiently retrieving accurate informationwhile maintaining strict privacy protocolsbecomes increasingly complex.
Scott Logic
FEBRUARY 12, 2025
Introduction Using Playwright snapshots with mocked data can significantly improve the speed at which UI regression is carried out. It facilitates rapid automated inspection of UI elements across the three main browsers (Chromium, Firefox, Webkit). You can tie multiple assertions to one snapshot, which greatly increases efficiency for UI testing. This type of efficiency is pivotal in a rapidly scaling GUI application.
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KDnuggets
FEBRUARY 12, 2025
Want to make the most out of large language models? Check out these prompting techniques you can start using today.
Monte Carlo
FEBRUARY 12, 2025
The large model is officially a commodity. In just two short years, API-based LLMs have gone from incomprehensible to smartphone accessible. The pace of AI innovation is slowing. Real world use cases are coming into focus. Going forward, the value of your genAI applications will exist solely in the fitnessand reliabilityof your own first-party data.
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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
KDnuggets
FEBRUARY 12, 2025
Discover freelancing platforms that care about you, not just your money, offering low commission rate, better policies, and higher earning potential.
Waitingforcode
FEBRUARY 12, 2025
After publishing a release of my blog post about the insertInto trap, I got an intriguing question in the comments. The alternative to the insertInto, the saveAsTable method, doesn't work well on partitioned data in overwrite mode while the insertInto does. True, but is there an alternative to it that doesn't require using this position-based function?
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
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databricks
FEBRUARY 12, 2025
We are excited to announce the second edition of the Databricks AI Security Framework (DASF 2.0 download now )! Organizations racing to harness.
Edureka
FEBRUARY 12, 2025
If you know a lot about computers or are just starting, you have probably come across Full Stack Developer and Software Engineer roles. At first look, they may appear extremely similar. Of course, they aren’t synonymous. But what separates them? More importantly, which one do your goals better align with? In this blog on Full Stack Developers vs Software Engineers, we’ll look at their main differences.
WeCloudData
FEBRUARY 12, 2025
Data scientists and Machine Learning engineers are both hot careers to follow with the recent advancement in technology. Both of these domains, data scientist vs machine learning engineer, are in high demand in any data-driven organization. Although data scientists and ML engineers share common ground in building models and handling data, they have differences in […] The post Data Scientist vs Machine Learning Engineer appeared first on WeCloudData.
Edureka
FEBRUARY 12, 2025
Bidirectional Encoder Representations from Transformers, or BERT, is a game-changer in the rapidly developing field of natural language processing (NLP). Built by Google, BERT revolutionizes machine learning for natural language processing, opening the door to more intelligent search engines and chatbots. The design, capabilities, and impact of BERT on altering NLP applications across industries are explored in this blog.
Speaker: Tamara Fingerlin, Developer Advocate
Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.
databricks
FEBRUARY 12, 2025
Utilities Of Today Todays power grid traces its roots back to the late 1800s when Pearl Street Station first serviced a handful of.
Edureka
FEBRUARY 12, 2025
An innovative artificial intelligence model, Stable Diffusion, can turn plain text into beautiful, high-quality pictures. This open-source application has revolutionized AI-driven creativity with its powerful deep-learning techniques. Stable Diffusion makes it easy and efficient—even on consumer-grade hardware—to generate original artwork, improve current photos, or investigate novel applications.
Confluent
FEBRUARY 12, 2025
Learn how Confluent Champion Suguna motivates her team of engineers to solve complex problems for customerswhile challenging herself to keep growing as a manager.
Robinhood
FEBRUARY 12, 2025
Robinhood Markets, Inc. (Nasdaq: HOOD) today reported financial results for the quarter ended December 31, 2024 and FY24. Read our Q4 and Full Year 2024 earnings press release here. Access more information at investors.robinhood.com. The post Robinhood Reports Fourth Quarter and Full Year 2024 Results appeared first on Robinhood Newsroom.
Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage
There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.
Edureka
FEBRUARY 12, 2025
LangChain is a dynamic framework designed to supercharge the potential of Large Language Models (LLMs) by seamlessly integrating them with tools, APIs, and memory. It empowers developers to craft intelligent and context-aware applications, from conversational AI to workflow automation. With its modular design and versatile capabilities, LangChain transforms static LLMs into powerful engines for innovation.
Snowflake
FEBRUARY 12, 2025
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Edureka
FEBRUARY 12, 2025
The Diffusion Library is your way of using AI for creative ideas. It allows you to create amazing pictures from nothing by using noise and text prompts, thanks to strong models like Stable Diffusion. With easy-to-use APIs and ready-made models, it’s an essential tool for anyone interested in generative AI and transforming random noise into art.
Data Engineering Weekly
FEBRUARY 12, 2025
Artificial Intelligence (AI) is at a turning point. For decades, conversations about Artificial General Intelligence (AGI) have been met with skepticism. Yet, recent breakthroughs in model architectures, memory management, and continual learning suggest that our machines are becoming ever more capable. This article traces a timeline of key innovations, illustrating how we have moved from simple language model reasoning to interactive , context-rich , and self-improving AI agents.
Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives
Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri
Edureka
FEBRUARY 12, 2025
What is a Variational Autoencoder? A Variational Autoencoder (VAE) is a type of generative model that learns to make new data by modeling the probability distribution of the data it is given. The standard autoencoder uses a neural network to turn input data into a fixed-size code and then read it back. This is an addition to that code. A VAE, on the other hand, doesn’t just make a fixed model of the latent data; instead, it maps the input data to a probabilistic distribution (usually Gauss
Confluent
FEBRUARY 12, 2025
Build high throughput Kafka applications using Confluent and Provisioned Mode for Lambda event source mapping (ESM)
Tweag
FEBRUARY 12, 2025
When I first joined the Topiary Team, I floated the idea of trying to format Bash with Topiary. While this did nothing to appease my unenviable epithet of the Bash guy, it was our first foray into expanding Topiarys support beyond OCaml and simple syntaxes like JSON. Alas, at the time, the Tree-sitter Bash grammar was not without its problems. I got quite a long way, despite this, but there were too many things that didnt work properly for us to graduate Bash to a supported language.
Precisely
FEBRUARY 12, 2025
Precisely has been hosting quarterly Automate User Group events. The Precisely team showed up in Chicago, IL, to facilitate a day of learning and engagement, complete with customer presentations, demos, and Q&A discussions. During the event, we were able to capture some key insights from Precisely customers, Generac Power Systems and Medela , and learn more about the different ways that they leverage Precisely Automate across their business operations.
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With Airflow being the open-source standard for workflow orchestration, knowing how to write Airflow DAGs has become an essential skill for every data engineer. This eBook provides a comprehensive overview of DAG writing features with plenty of example code. You’ll learn how to: Understand the building blocks DAGs, combine them in complex pipelines, and schedule your DAG to run exactly when you want it to Write DAGs that adapt to your data at runtime and set up alerts and notifications Scale you
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