Anaconda
Sometimes used as shorthand for the Anaconda Distribution, Anaconda, Inc.
is the company behind Anaconda Distribution, conda, conda-build and
Anaconda Enterprise.
Anaconda.org
A cloud package repository hosting service at https://www.anaconda.org.
With a free account, you can publish packages you create to be used
publicly.
Anaconda Distribution
Open source repository of hundreds of popular data science packages,
along with the conda package and virtual environment manager for Windows,
Linux, and MacOS. Conda makes it quick and easy to install, run, and
upgrade complex data science and machine learning environments like
scikit-learn, TensorFlow, and SciPy.
Anaconda Enterprise
A software platform for developing, governing, and automating data
science and AI pipelines from laptop to production. Enterprise enables
collaboration between teams of thousands of data scientists running
large-scale model deployments on high-performance production clusters.
Anaconda Navigator
A desktop Graphical User Interface (GUI) included in Anaconda
Distribution that allows you to easily use and manage IDEs, conda
packages, environments, channels, and notebooks without the need to use
the Command Line Interface (CLI).
Anaconda project
An encapsulation of your data science assets to make them easily
portable. Projects may include files, environment variables, runnable
commands, services, packages, channels, environment specifications,
scripts, and notebooks. Each project also includes an
anaconda-project.yml configuration file to automate setup, so you
can easily run and share it with others. You can create and configure
projects from the Enterprise web interface or command line interface.
Channel
A location in the repository where Anaconda Enterprise looks for
packages. Enterprise Administrators and users can define channels,
determine which packages are available in a channel, and restrict access
to specific users or groups.
Commit
To make a set of local changes permanent by copying them to the remote
server. Anaconda Enterprise checks to see if your work will conflict
with any commits that your colleagues have made on the same project,
so the files will not be overwritten unless you so choose to do so.
Conda
An open source package and environment manager that makes it quick and
easy to install, run, and upgrade complex data science and machine
learning environments like scikit-learn, TensorFlow, and SciPy. Thousands
of Python and R packages can be installed with conda on Windows, MacOS X,
Linux and IBM Power.
Conda-build
A tool used to build conda packages from recipes.
Conda environment
A superset of Python virtual environments, conda environments make it
easy to create projects with different versions of Python and avoid
issues related to dependencies and version requirements. A conda
environment maintains its own files, directories, and paths so that you
can work with specific versions of libraries and/or Python itself without
affecting other Python projects.
Conda package
A binary tarball file containing system-level libraries, Python and R
modules, executable programs, or other components. Conda tracks
dependencies between specific packages and platforms, making it simple to
create operating system-specific environments using different
combinations of packages.
Conda recipe
Instructions used to tell conda-build how to build a package.
Deployment
A deployed Anaconda project containing a Notebook, web app, dashboard or
machine learning model (exposed via an API). When you deploy a project,
Anaconda Enterprise builds a container with all the required dependencies
and runtime components—the libraries on which the project depends in
order to run—and launches it with the security and access permissions
defined by the user. This allows you to easily run and share the
application with others.
Interactive data application
Visualizations with sliders, drop-downs and other widgets that allow
users to interact with them. Interactive data applications can drive new
computations, update plots and connect to other programmatic
functionality.
Interactive development environment (IDE)
A suite of software tools that combines everything a developer needs
to write and test software. It typically includes a code editor,
a compiler or interpreter, and a debugger that the developer accesses
through a single Graphical User Interface (GUI).
An IDE may be installed locally, or it may be included as part of
one or more existing and compatible applications accessed through a web
browser.
Jupyter
A popular open source IDE for building interactive Notebooks by the
Jupyter Foundation.
JupyterHub
An open source system for hosting multiple Jupyter Notebooks in a
centralized location.
JupyterLab
Jupyter Foundation’s successor IDE to Jupyter, with flexible building
blocks for interactive and collaborative computing. For Jupyter Notebook
users, the interface for JupyterLab is familiar and still contains the
notebook, file browser, text editor, terminal, and outputs.
Jupyter Notebook
The default browser-based IDE available in Anaconda Enterprise.
It combines the notebook, file browser, text editor, terminal and outputs.
Live notebook
JupyterLab and Jupyter Notebooks are web-based IDE applications that
allow you to create and share documents that contain live code in R or
Python, equations, visualizations, and explanatory text.
Package
Software files and information about the software—such as its name,
description, and specific version—bundled into a file that can be
installed and managed by a package manager. Packages can be encapsulated
into Anaconda projects for easy portability.
Project template
Contains all the base files and components to support a particular
programming environment. For example, a Python Spark project template
contains everything you need to write Python code that connects to Spark
clusters. When creating a new project, you can select a template that
contains a set of packages and their dependencies.
Repository
Any storage location from which software or software assets may be retrieved and installed on a local computer.
REST API
A common way to operationalize a machine learning model is through a REST
API. A REST API is a web server endpoint, or callable URL, which provides
results based on a query. REST APIs allow developers to create
applications that incorporate machine learning and prediction, without
having to write models themselves.
Session
An open project, running in an editor or IDE.
Spark
A distributed SQL database and project of the Apache Foundation. While
Spark has historically been tightly associated with Apache Hadoop and run
on Hadoop clusters, recently the Spark project has sought to separate
itself from Hadoop by releasing support for Spark on Kubernetes. The core
data structure in Spark is the RDD (Resilient Distributed Dataset)—a
collection of data types, distributed in redundant fashion across many
systems. To improve performance, RDDs are cached in memory by default,
but can also be written to disk for persistence. Spark Ignite is a
project to offer Spark RDDs that can be shared in-memory across
applications.