The 5 Classification Evaluation Metrics Every Data Scientist Must Know
KDnuggets
OCTOBER 16, 2019
This post is about various evaluation metrics and how and when to use them.
KDnuggets
OCTOBER 16, 2019
This post is about various evaluation metrics and how and when to use them.
Uber Engineering
OCTOBER 16, 2019
Michelangelo , Uber’s machine learning (ML) platform, supports the training and serving of thousands of models in production across the company. Designed to cover the end-to-end ML workflow, the system currently supports classical machine learning, time series forecasting, and deep … The post Evolving Michelangelo Model Representation for Flexibility at Scale appeared first on Uber Engineering Blog.
Data Engineering Podcast
OCTOBER 14, 2019
Summary Managing a data warehouse can be challenging, especially when trying to maintain a common set of patterns. Dataform is a platform that helps you apply engineering principles to your data transformations and table definitions, including unit testing SQL scripts, defining repeatable pipelines, and adding metadata to your warehouse to improve your team’s communication.
Confluent
OCTOBER 16, 2019
Trains are an excellent source of streaming data—their movements around the network are an unbounded series of events. Using this data, Apache Kafka ® and Confluent Platform can provide the foundations for both event-driven applications as well as an analytical platform. With tools like KSQL and Kafka Connect, the concept of streaming ETL is made accessible to a much wider audience of developers and data engineers.
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Apache Airflow® is the open-source standard to manage workflows as code. It is a versatile tool used in companies across the world from agile startups to tech giants to flagship enterprises across all industries. Due to its widespread adoption, Airflow knowledge is paramount to success in the field of data engineering.
KDnuggets
OCTOBER 16, 2019
A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
Netflix Tech
OCTOBER 16, 2019
By Ammar Khaku Introduction In a microservice architecture such as Netflix’s, propagating datasets from a single source to multiple downstream destinations can be challenging. These datasets can represent anything from service configuration to the results of a batch job, are often needed in-memory to optimize access and must be updated as they change over time.
Teradata
OCTOBER 15, 2019
Find out how our UX team is going to radically simplify the Teradata user experience. To be unveiled at Teradata Universe!
KDnuggets
OCTOBER 17, 2019
This post aims to make you get started with putting your trained machine learning models into production using Flask API.
Dataquest
OCTOBER 16, 2019
Exciting news: we just launched a totally revamped Data Engineering path that offers from-scratch training for anyone who wants to become a data engineer or learn some data engineering skills. Looks cool, right? But it begs the question: why learn data engineering in the first place? Typically, data science teams are comprised of data analysts, data scientists, and data engineers.
Speaker: Tamara Fingerlin, Developer Advocate
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!
Netflix Tech
OCTOBER 18, 2019
Faisal Siddiqi Infrastructure for Contextual Bandits and Reinforcement Learning?—? theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches can be seen as a stepping stone to full-on Reinforcement Learning (RL) with closed-loop, on-policy evaluation and model objec
KDnuggets
OCTOBER 14, 2019
As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it’s the right approach for a given problem.
KDnuggets
OCTOBER 17, 2019
While the average salary for a Software Engineer is around $100,000 to $150,000, to make the big bucks you want to be an AI or Machine Learning (Specialist/Scientist/Engineer.).
KDnuggets
OCTOBER 18, 2019
Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras.
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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.
KDnuggets
OCTOBER 18, 2019
If you want to launch your data science skills into freelance work, then check out these important tips to help you kick start your next adventure in data.
KDnuggets
OCTOBER 14, 2019
Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.
KDnuggets
OCTOBER 14, 2019
Density estimation is estimating the probability density function of the population from the sample. This post examines and compares a number of approaches to density estimation.
KDnuggets
OCTOBER 15, 2019
In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video.
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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?
KDnuggets
OCTOBER 16, 2019
Learn about one of the fundamental theorems of probability with an easy everyday example.
KDnuggets
OCTOBER 15, 2019
The way we control our data isn’t working. Data is as vulnerable as ever. Download this white paper, which outlines lessons about how data science and governance programs can, if implemented properly, reinforce each other’s objective.
KDnuggets
OCTOBER 15, 2019
Even though I’m still in my studies, here’s a list of the most important things I’ve learned (as of yet).
KDnuggets
OCTOBER 18, 2019
In this second part we want to outline our own experience building an AI application and reflect on why we chose not to utilise deep learning as the core technology used.
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With over 30 million monthly downloads, Apache Airflow is the tool of choice for programmatically authoring, scheduling, and monitoring data pipelines. Airflow enables you to define workflows as Python code, allowing for dynamic and scalable pipelines suitable to any use case from ETL/ELT to running ML/AI operations in production. This introductory tutorial provides a crash course for writing and deploying your first Airflow pipeline.
KDnuggets
OCTOBER 17, 2019
While effective anonymization technology remains elusive, understanding the history of this challenge can guide data science practitioners to address these important concerns through ethical and responsible use of sensitive information.
KDnuggets
OCTOBER 14, 2019
A recent survey outlined the main neural architecture search methods used to automate the design of deep learning systems.
KDnuggets
OCTOBER 15, 2019
Follow this step-by-step tutorial using Tensorflow to setup a DC/OS Data Science Engine as a PaaS for enabling distributed multi-node, multi-GPU model training.
KDnuggets
OCTOBER 16, 2019
This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.
Speaker: Jay Allardyce, Deepak Vittal, Terrence Sheflin, and Mahyar Ghasemali
As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.
KDnuggets
OCTOBER 16, 2019
Also: Kannada-MNIST: A new handwritten digits dataset in ML town; Math for Programmers; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; The Last SQL Guide for Data Analysis You’ll Ever Need.
Dataquest
OCTOBER 16, 2019
We’ve got some really exciting news: we’ve just launched a total revamp of our Data Engineering learning path ! This revamped path is designed to be more like our other course paths. You can start it even if you have no prior experience with coding , and it’ll take you from total beginner to experienced practitioner with all of the core skills needed to become a data engineer.
KDnuggets
OCTOBER 18, 2019
ODSC West comes to San Francisco on Oct 29 - Nov 1. With over 300 hours of content, 200+ speakers, and thousands of attendees, there is certainly a lot to see, learn, and do at the conference. Register by Friday for 10% off your pass.
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