Anaconda Reviews

We primarily use the solution for deep learning and machine learning.

The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly. 

We extensively use TensorFlow and Pytorch as the modules. 

I have nothing to say about improvements; I love this product. It's our bread and butter and we use it every day.

We did have issues with virtual environments in the past, but that has worked itself out.

I've been using the solution for 15 years.

The solution is pretty stable. A lot of our production is on Anaconda only.

The scalability of the solution is good. We have about 100 technicians on it. At this time, we don't plan to increase usage.

We've never had to reach out to technical support.

We did not use a different solution.

The initial setup was straightforward. Deployment takes a few hours.

We handled the deployment ourselves.

We use various deployment models but mostly work with hybrid models.

We don't rely on Anaconda's deployment defense a lot. We use the solution mainly for Python distribution and deployment monitoring internals. We deploy in Docker and scale it.

It's one of the best tools available out there. If you have to get started very quickly, it's great. Almost everything is ready for you to use. I think it's a wonderful tool for developers to get started with.

I'd rate the solution nine out of ten.

In Anaconda, we get everything: RStudio, Spyder, and Jupyter. R Studio is for R, and Spyder and Jupyter are for Python. Using these, we will be doing data wrangling and data modeling for a developing project.

It's interesting. It's user friendly. That's what makes it outstanding among the other competitors.

It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science.

It crashes once in a while. In case of a reboot or something unexpected, the unseen code part will get diminished, and it relatively takes longer than other applications when a reboot is happening. They can improve in these areas. 

They can also bring some database software. They have software for analytics and virtualization. However, they don't have any software for the database.

I have been using this solution for the last one year since I joined this company. It was suggested by some of my seniors because it would be better for the database and a one-stop solution that pays for all things.

It is stable. 

I didn't get an opportunity to test this feature. I haven't yet come across an area where I can test the scalability of this platform.

A lot of people who work for data science projects will use Anaconda on a daily basis or at least twice or thrice a week. I use Anaconda almost daily, like for at least half an hour daily. On some days, it can also be for five, six hours.

There weren't many issues for which I needed support from external people. So far, it's good. 

It's easy to set up. You download the EXT file and follow the instructions. It's as simple as that. It's not a big thing. It took around five minutes.

I would recommend it to anyone willing to work in data science. This will be a starting place that covers data-wrangling aspects, user relation aspects, and everything. It is a one-stop solution for everything. 

Anaconda is the main go-to place for analytics. This solution is very handy for almost all data science people. A lot of people I know nowadays use Anaconda. I don't think any other product can even come near Anaconda for data science.

I would rate Anaconda a nine out of ten. The long reboot time and once in a while crash are the two things that lack in Anaconda. Apart from that, I don't see any issues with Anaconda.

The best platform for a data scientist for development purposes. It supports applications which are needed for data analytics like Jupiter and predictive analytics like R.

It creates a good environment for a data science student to teach predictive and data analytics on one platform. 

Many data science applications on one single platform. This provides a platform for learning and development in the data science domain.

• Currently, it's working perfectly on Microsoft Windows but lags in open source OS like Ubuntu, Mint.
• File uploading feature needs to be improved.

Stability is good.

Scalability is the best.

Technical support is good.

Previously, we were using RStudio, PyCharm for data science domain, but with this software, we've got a perfect platform to teach data science.

The initial setup was complex.

In-house one.

Good.

We did not evaluate other options.