This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
This book is thought for beginners in Machine Learning, that are looking for a practical approach to learning by building projects and studying the different Machine Learning algorithms within a specific context.
This article show how you can offload data from on-premises transactional (OLTP) databases to cloud-based datastores, including Snowflake and Amazon S3 with Athena. I’m also going to take the opportunity […].
Summary Databases are useful for inspecting the current state of your application, but inspecting the history of that data can get messy without a way to track changes as they happen. Debezium is an open source platform for reliable change data capture that you can use to build supplemental systems for everything from maintaining audit trails to real-time updates of your data warehouse.
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.
The standard job description for a Data Scientist has long highlighted skills in R, Python, SQL, and Machine Learning. With the field evolving, these core competencies are no longer enough to stay competitive in the job market.
Following Google’s announcement to provide leading open source services with a cloud-native experience by partnering with companies like Confluent, we are delighted to share that Confluent Cloud is now available […].
At Grouparoo , we have been interviewing a lot of marketers. The overall learning is that it's a hard job. The biggest reason is that they need data to make their campaigns work and do not have the means to get that data. Basically, they need Engineers to prioritize writing code to get the data into the tool they are using. That rarely happens.
At Grouparoo , we have been interviewing a lot of marketers. The overall learning is that it's a hard job. The biggest reason is that they need data to make their campaigns work and do not have the means to get that data. Basically, they need Engineers to prioritize writing code to get the data into the tool they are using. That rarely happens.
Performing ad-hoc analysis is a daily part of life for most data scientists and analysts on operations teams. They are often held back by not having direct and immediate access to their data because the data might not be in a data warehouse or it might be stored across multiple systems in different formats. This typically means that a data engineer will need to help develop pipelines and tables that can be accessed in order for the analysts to do their work.
Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.
An estimated 8,650% growth of the volume of Data to 175 zetabytes from 2010 to 2025 has created an enormous need for Data Engineers to build an organization's big data platform to be fast, efficient and scalable.
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.
A resume plays a key role in bagging that dream data science job. We break down the nuances of a job-winning data science resume so that you can go ahead and transform your own resume.
Ethics in AI has received significant attention recently, and the new KDnuggets cartoon examines the problem of teaching ethics to artificially intelligent entities.
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
Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data.
Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently.
Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks.
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
Finding a deep learning model to perform well is an exciting feat. But, might there be other -- less complex -- models that perform just as well for your application? A simple complexity measure based on the statistical physics concept of Cascading Periodic Spectral Ergodicity (cPSE) can help us be computationally efficient by considering the least complex during model selection.
The healthcare AI market is expected to reach 28 billion dollars by the year 2025. With such exponential growth, AI is undoubtedly likely to bring some drastic changes in the healthcare industry. Let’s look at five ways of how AI has changed the healthcare industry.
Breaking into a career in Data Science can depend on where you start. See if you fit into one of these three categories of "newbies," and then find out how to make your professional transition into the field.
There is a need for a new way to explain complex, ensembled ML models for high-stakes applications such as credit and lending. This is why we invented GIG.
In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!
Introduction to Data Visualization & Storytelling;The Data Science Interview Study Guide; Why Kaggle will NOT make you a great Data Scientist; Cartoon: Teaching Ethics to AI.
In this webinar, Jan 15 @ 12PM EST, we'll offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline. Register now to attend live or to watch a recording afterwards.
Earn a Master of Professional Studies in Data Analytics online through Penn State World Campus – and you can add in-demand skills to your wheelhouse while you continue to work.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
Here are our top five hands-on training focus areas that every data scientist should know and that we’re paying extra attention to at ODSC East 2020 this April 13-17 in Boston.
First issue of 2020 brings you a summary of how to "Ultralearn" Data Science - for those in a hurry; Explains how teams work on AutoML project; Why Python is a preferred language for Data Science; and a cartoon on teaching ethics to AI.
Also: Predict Electricity Consumption Using Time Series Analysis; What is the most important question for Data Science (and Digital Transformation); Why Python is One of the Most Preferred Languages for Data Science?; What is a Data Scientist Worth?; How to Speed up Pandas by 4x with one line of code.
We organize all of the trending information in your field so you don't have to. Join 37,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content