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Anaconda

Anaconda

Overview

What is Anaconda?

Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research.…

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Learn from top reviewers

Must Have for ML/DL, Data Analytics, Software Development and Deployment.

Rating: 10 out of 10
December 18, 2024
IncentivizedVetted ReviewVerified User
We're using Anaconda for software further software for our clients. Earlier, I used both R and Python, but now I am mainly using it for Python. As we have multiple application...
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Ranu Singh
Manager - Research & Development
Technology Manager
Kainoos
Anaconda7 years of experience

The best and easiest data analysis tool

Rating: 10 out of 10
July 5, 2022
Vetted ReviewVerified User
Since my beginnings in the programming path I have been using it, and it is very difficult to do without it. It provided me with some software that I needed such as Spyder Edi...
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Ammar Aboalrub
Employee - Customer Service
Freelancer
Self Employed
Anaconda3 years of experience

Best IDE for Data Science Projects

Rating: 8 out of 10
October 7, 2021
IncentivizedVetted ReviewVerified User
Anaconda is the best tool for the data scientist to [develop] the machine learning project [under] a single umbrella. It is used [throughout] the whole organization. We are us...
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Zayed Rais
Professional - Information Technology
Program Analyst
Cognizant
Anaconda2 years of experience

One stop data science destination - Anaconda

Rating: 9 out of 10
June 25, 2021
IncentivizedVetted ReviewVerified User
My previous organization used Anaconda, Jupyter Notebook specifically to run sales forecasting codes in Python. At the time, it was specifically used by the E-Commerce and Buy...
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Rounak Verma
Employee - Engineering
Data Analyst
Shipt
Anaconda3 years of experience

Anaconda for Data Science!

Rating: 10 out of 10
June 12, 2021
IncentivizedVetted ReviewVerified User
Anaconda is being used by the Business Intelligence and Data Science department at my organization. It is used widely for analysis, forecasting and answering questions. For ex...
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Anomita Chandra
Supervisor - Other
Business Analyst
University of Texas at Dallas
Anaconda4 years of experience
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Pricing

View all pricing

Free Tier

$0

Cloud
per month

Starter Tier

$9

Cloud
per month

Business Tier

$50

Cloud
per month per user

Entry-level set up fee?

  • No setup fee
For the latest information on pricing, visitwww.anaconda.com/pricing

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
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Product Demos

Introducing Anaconda Distribution for Python in Excel

YouTube

Introducing: Anaconda Assistant

YouTube

Anaconda for Open-Source Security with Python and R

YouTube

AI Development in the Enterprise with Anaconda's Data Science Platform

YouTube
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Features

Platform Connectivity

Ability to connect to a wide variety of data sources

9.3
Avg 8.3

Data Exploration

Ability to explore data and develop insights

8.5
Avg 8.4

Data Preparation

Ability to prepare data for analysis

9.1
Avg 8.2

Platform Data Modeling

Building predictive data models

9.2
Avg 8.4

Model Deployment

Tools for deploying models into production

9.5
Avg 8.5
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Product Details

What is Anaconda?

Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.


Anaconda Features

Platform Connectivity Features

  • Supported: Extend Existing Data Sources

Data Exploration Features

  • Supported: Visualization
  • Supported: Interactive Data Analysis

Data Preparation Features

  • Supported: Data Transformations
  • Supported: Data Encryption

Platform Data Modeling Features

  • Supported: Multiple Model Development Languages and Tools
  • Supported: Automated Machine Learning
  • Supported: Single platform for multiple model development
  • Supported: Self-Service Model Delivery

Model Deployment Features

  • Supported: Flexible Model Publishing Options
  • Supported: Security, Governance, and Cost Controls

Anaconda Integrations

Anaconda Competitors

Anaconda Technical Details

Deployment TypesOn-premise, Software as a Service (SaaS), Cloud, or Web-Based
Operating SystemsWindows, Linux, Mac
Mobile ApplicationNo
Supported CountriesGlobal

Frequently Asked Questions

Dataiku, Domino Enterprise MLOps Platform, and Posit are common alternatives for Anaconda.

Reviewers rate Single platform for multiple model development and Flexible Model Publishing Options highest, with a score of 10.

The most common users of Anaconda are from Enterprises (1,001+ employees).
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Comparisons

View all alternatives
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Reviews From Top Reviewers

View all reviews
(1-3 of 3)

Must Have for ML/DL, Data Analytics, Software Development and Deployment.

Rating: 10 out of 10
Incentivized
December 18, 2024
RS
Ranu Singh
Technology Manager
Kainoos (Information Technology & Services, 51-200 employees)
Vetted Review
Verified User
Anaconda
7 years of experience
Use Cases and Deployment Scope
We're using Anaconda for software further software for our clients. Earlier, I used both R and Python, but now I am mainly using it for Python. As we have multiple applications running on multiple Python versions ranging from Python 2.x to 3.x. and with Anaconda, this becomes relatively easy with its environments. I am actively using Spyder, PyCharm, and Jupyter Notebook. Apart from this, we are actively using Anaconda on our servers to deploy any machine learning applications.
Pros
  • Data Analysis.
  • Software Development in Python.
  • Machine Learning/Deep Learning model training and testing.
  • Code Deployments.
Cons
  • Sometimes, I have reached a situation where I am unable to download dependency using pip or conda, and I have to create whole new environments.
  • Once, I faced a very weird issue where I was unable to update or Launch Spyder and tried everything, and it didn't work.
Likelihood to Recommend
I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.
Anaconda Feature Ratings
Platform Connectivity (1)
80%
8.0
Extend Existing Data Sources
80%
8.0
Data Exploration (2)
85%
8.5
Visualization
90%
9.0
Interactive Data Analysis
80%
8.0
Data Preparation (2)
40%
4.0
Data Transformations
80%
8.0
Data Encryption
N/A
N/A
Platform Data Modeling (4)
70%
7.0
Multiple Model Development Languages and Tools
90%
9.0
Automated Machine Learning
N/A
N/A
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
90%
9.0
Model Deployment (2)
95%
9.5
Flexible Model Publishing Options
100%
10.0
Security, Governance, and Cost Controls
90%
9.0
Return on Investment
  • We're using Anaconda as open source, so it has only given us returns/profits, so there is no negative here.
Usability
9
I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.
Alternatives Considered
  • Docker
I am using both; when it comes to application deployment on the server, I use Docker, and sometimes, I use Docker with conda image for deployment when it comes to ML/DL apps.
Other Software Used

Good way to standardize Python-based developments & support for desktop users.

Rating: 9 out of 10
Incentivized
March 17, 2020
JS
Juande Santander-Vela
Software Development Lead / Product Manager at Solution Level
SKA Organisation (Research, 51-200 employees)
Vetted Review
Verified User
Anaconda
2 years of experience
View profile
Use Cases and Deployment Scope
The usage of Anaconda is not yet standard through the organization, but many people at my organization use it as the best way to create a standardized Python environment. In particular, the Miniconda distribution is preferred for deployment of Python-based containers, as it allows for a better, finer-grained installation in containers. For desktop users, the full Anaconda distribution is used, as it comes with several packages that are used throughout the organization: Astropy, NumPy, Matplotlib, Pandas, and others.
Pros
  • Management of custom environments
  • Support for standardizing deployments
  • Deployment in containers using Miniconda
Cons
  • Update of Conda packages is becoming slower. The 4.7 update was welcome, but seems to be regressing again.
Likelihood to Recommend
Anaconda, and Miniconda, are easy to deploy, scientifically-ready Python distributions, especially well-suited for the fields of science, astronomy, and engineering. We are using Miniconda for finer-grained customization of environments in containers for deployment.

We are not using the customer supported version of Anaconda, and instead, we are relying on the community edition, based on the Open Source of all of our software. Hence, I am not evaluating Anaconda's support. Also, we are not making use as a company of the multi-language support in Anaconda, but I have tried the SciJava, R, and Julia support in Anaconda.
Anaconda Feature Ratings
Platform Connectivity (4)
45%
4.5
Connect to Multiple Data Sources
90%
9.0
Extend Existing Data Sources
90%
9.0
Automatic Data Format Detection
N/A
N/A
MDM Integration
N/A
N/A
Data Exploration (2)
90%
9.0
Visualization
90%
9.0
Interactive Data Analysis
90%
9.0
Data Preparation (4)
80%
8.0
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
90%
9.0
Data Encryption
70%
7.0
Built-in Processors
70%
7.0
Platform Data Modeling (4)
65%
6.5
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
70%
7.0
Single platform for multiple model development
90%
9.0
Self-Service Model Delivery
N/A
N/A
Model Deployment (2)
N/A
N/A
Flexible Model Publishing Options
N/A
N/A
Security, Governance, and Cost Controls
N/A
N/A
Return on Investment
  • Anaconda (and especially Miniconda) has simplified our deployment strategy.
Alternatives Considered
Anaconda has 64-bit support in the community edition, and package management is more in line with the way we think.
Support Rating
10
There are a lot of materials allowing you to get self-service support with Anaconda. I have not tried any support tier for Anaconda.
Other Software Used
Easy Tasks
  • Environment Update
  • Package Management
Difficult Tasks
  • Managing non-standard channels
Mobile Interface Availability and Impressions
No
Usability
9
The interface is an easy to use command-line interface, or a GUI for launching and/or discovering different parts of the system.

Anaconda: The Data Science Starter Kit.

Rating: 7 out of 10
Incentivized
March 13, 2019
MD
Matthew Deakyne
Academic Services Manager
The University of Kansas (Higher Education, 10,001+ employees)
Vetted Review
Verified User
Anaconda
3 years of experience
View profile
Use Cases and Deployment Scope
Anaconda is being used for data analysis purposes. We use it to mainly manage python distributions and to preload scientific packages that make working with data very easy. It's used by pockets on campus, mostly those who have research needs. It relieves us from having to purchase expensive software like SAS or SPSS and uses both Python and R.
Pros
  • Clear install story. There are a lot of ways to install python. There's only one way to install anaconda. This makes teaching and standardizing much easier.
  • Batteries included. It's easy to install things in python, but anaconda ships with most of what you need out of the box. This helps with standardization and reproducibility.
  • Good integrations with Jupyter and other visual tools. Jupyter is really convenient when learning various python packages. Anaconda makes these tools easy to launch and to use.
Cons
  • Doesn't play well with other Python. I use python for more than data science, and whenever I have multiple versions of python on my machine —some using conda, some using Pipenv, some using poetry— it can get really confusing. If Anaconda is all you use, then it works really well.
  • Not all packages are available in Anaconda. Conda install doesn't always work for all PyPI packages. This adds to the frustration above - as you have to install some packages outside of conda, and then figure out how to use them internally.
  • Visualizations don't always work like you'd hope. This is getting better, but creating interactive graphics doesn't always work well in this context.
Likelihood to Recommend
Anaconda is excellent if all you do is data science. If you are already a python developer, then it may be more frustrating having multiple ways to manage your distribution and your packages. It has very clear use cases and makes starting off in data science much easier than figuring out all you'd need to install in Vanilla Python.
Anaconda Feature Ratings
Platform Connectivity (3)
70%
7.0
Connect to Multiple Data Sources
100%
10.0
Extend Existing Data Sources
80%
8.0
Automatic Data Format Detection
30%
3.0
Data Exploration (2)
75%
7.5
Visualization
90%
9.0
Interactive Data Analysis
60%
6.0
Data Preparation (3)
83.33333333333334%
8.3
Interactive Data Cleaning and Enrichment
90%
9.0
Data Transformations
100%
10.0
Data Encryption
60%
6.0
Platform Data Modeling (4)
95%
9.5
Multiple Model Development Languages and Tools
100%
10.0
Automated Machine Learning
90%
9.0
Single platform for multiple model development
100%
10.0
Self-Service Model Delivery
90%
9.0
Model Deployment (2)
85%
8.5
Flexible Model Publishing Options
90%
9.0
Security, Governance, and Cost Controls
80%
8.0
Return on Investment
  • Anaconda has helped us analyze academic data to inform business decisions.
  • Anaconda has helped us visualize complex data sets into understandable graphics.
  • Anaconda has helped us explore machine learning abilities and limitations.
Alternatives Considered
I prefer Anaconda due to the control I have at every level over the data and the visualizations. Power BI does a better job at guessing what graphics to use, but these usually aren't the most helpful. Anaconda and the slew of Python extensions that add incredible functionality, make it much more powerful without such a steep learning curve as Power BI.
Other Software Used
Users and Roles
15
Most of the users are researchers or analysts in some capacity. It is featured in Software Carpentry workshops, and so the usage has expanded to Academics and some Staff members.
Support Headcount Required
2
The two supporters in an ongoing basis are based in the Libraries, and their positions are to support data practices in research. They have functional programming skills, such as version control and unix command line expertise, as well as scientific programming skills such as R and Python. They instruct on how to install and use Anaconda in practical workshops, and also have consulting services.
Business Processes Supported
  • Data Analytics. Evaluating data sets.
  • Data Exploration. Visualizing relationships within data sets.
  • Reporting. Creating reproducible reports.
Innovative Uses
  • Introduction to programming environment. It works well as an introduction, as the install story is really clear.
  • Data literacy. Helping non-programmers and non-statisticians better understand the capabilities of machine learning and other computing techniques.
  • Data pipelines. Moving data from one system to another is handled by many different systems - anaconda and python make this fairly easy.
Future Planned Uses
  • Replacing programs such as Excel.
  • Generating interactive websites.
  • Automating reporting across the University.
Likelihood to Renew
7
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
Easy Tasks
  • Installation. This is the highlight of anaconda. It's really easy to install.
  • Launching interactive consoles. It integrates with Jupyter really well.
  • Getting started. All the libraries you'd need are included.
Difficult Tasks
  • Managing Python virtual environments.
  • Version control. You can use git, but better integration would be nice.
  • Developing interactive websites. It's not really intended for this.
Mobile Interface Availability and Impressions
No
Usability
8
It's really good at installing and getting started. It's less usable and configurable after that. If you stay in the ecosystem, and don't know how to Python any other way - it works really well.
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