The Anaconda Blog

Data Science Blog
Who You Gonna Call? Halloween Tips & Treats to Protect You from Ghosts, Gremlins…and Software Vulnerabilities

Happy Halloween, readers. At Anaconda, we’re not too scared about things that go bump in the night. We’ve examined the data and concluded that it’s just the cleaning staff upstairs. We are, however, kept awake…

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Data Science Blog
Patching Source Code to Conda Build Recipes

If you are a developer who relies upon conda, we hope to encourage you to begin building your own packages so that your projects can be used just like all of the other packages you…

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Company Blog
Anaconda Enterprise 5.2.2: Now With Apache Zeppelin and GPU improvements

Anaconda Enterprise 5.2 introduced exciting features such as GPU-acceleration, scalable machine learning, and cloud-native model management in July. Today we’re releasing Anaconda Enterprise 5.2.2 with a number of enhancements in IDEs (Integrated Development Environments), GPU resource…

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Company Blog
Bringing Dataframe Acceleration to the GPU with RAPIDS Open-Source Software from NVIDIA

Today we are excited to talk about the RAPIDS GPU dataframe release along with our partners in this effort: NVIDIA, BlazingDB, and Quansight. RAPIDS is the culmination of 18 months of open source development to…

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Data Science Blog
Intake: Parsing Data from Filenames and Paths

Motivation Do you have data in collections of files, where information is encoded both in the contents and the file/directory names? Perhaps something like  ‘{year}/{month}/{day}/{site}/measurement.csv’ ? This is a very common problem for which people…

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Company Blog
Anaconda Distribution 5.3.0 Released

We’re excited to announce the release of Anaconda Distribution 5.3.0! Anaconda Distribution is the world’s most popular and easiest way to learn and perform data science and machine learning. Here’s a rundown of new features.…

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Data Science Blog
Open Source Model Management Roundup: Polyaxon, Argo, and Seldon

One of the most common questions the Anaconda Enterprise team receives is something along the lines of: “But really, how difficult is it to build this using open source tools?” This is certainly a fair…

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Data Science Blog
Intake: Caching Data on First Read Makes Future Analysis Faster

By Mike McCarty Intake provides easy access data sources from remote/cloud storage. However, for large files, the cost of downloading files every time data is read can be extremely high. To overcome this obstacle, we have…

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Company Blog
AI Enablement Platform for Teams at Scale—Accelerate Your AI/ML Productivity with Anaconda Enterprise and Cisco UCS

By Daniel Rodriguez Anaconda Enterprise is a software platform for developing, governing, and automating data science and machine learning pipelines from laptop to production. It is the de-facto standard for data science and machine learning,…

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Data Science Blog
TensorFlow in Anaconda

By Jonathan Helmus TensorFlow is a Python library for high-performance numerical calculations that allows users to create sophisticated deep learning and machine learning applications. Released as open source software in 2015, TensorFlow has seen tremendous…

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Data Science Blog
Python 3.7 Package Build Out & Miniconda Release

By Ray Donnelly & Crystal Soja We are pleased to announce that Python 3.7 packages for all supported platforms and packages of the Anaconda Distribution Repository (repo.anaconda.com) are now available. There are 865 packages built…

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Data Science Blog
Distributed Auto-ML with TPOT with Dask

By Tom Augspurger This work is supported byAnaconda, Inc. This post describes a recent improvement made to TPOT. TPOT is an automated machine learning library for Python. It does some feature engineering and hyper-parameter optimization for you. TPOT…

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