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.
$0
per month
AWS CodeArtifact
Score 9.3 out of 10
N/A
AWS CodeArtifact is a fully managed artifact repository service that aims to make it easy for organizations of any size to securely store, publish, and share software packages used in their software development process. CodeArtifact can be configured to automatically fetch software packages and dependencies from public artifact repositories so developers have access to the latest versions.
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.
We have a small team with limited resources and it worked well for us. Hence I can conclude that AWS Code Artifact are well suited for organizations which have limited resources in terms of hardware and access to administrators for setting up artifact repository in-house. AWS Code Artifact is also suited particularly well for organization(s) which are already using AWS Services/Infrastructure (eg. EC2) . It works quite well with existing AWS services and completes the gap which existed in AWS offering for quite some time. Organizations can move their entire DevOps toolchain and infrastructure to Amazon. It is less appropriate for organization(s) which rely on artifacts like Debian, C/C++, Go etc as AWS does not support those fully.
Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.
I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.
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.
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.
Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!
AWS CodeArtifact is an excellent choice for organization(s) which are looking to move their infrastructure and devops toolchain to Amazon. It is very useful for teams/organizations on limited budget or do not want to take on infrastructure and maintenance costs associated with the artifact repository. Other software solutions require resources for setting up and need ongoing maintenance.
It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.
Overall CodeArtifact has positive ROI on the our team. We had limited budget for procurement of server/administrators. With CodeArtifact we were able to get some savings.
We were able to deliver faster hence customers were quite happy. That led to customer satisfaction
We didnt have to invest on maintaining network infrastructure/uptime and security. That saved us quite a bit of hassle and funds.
