Anaconda Business Reviews

I use the solution for learning purposes only. I don't use it for any production standard quota, and have not deployed it.

When I try to develop an implementer model, I can just do some trial and error and see the output immediately. Then later I can move them onto the Python file. Until that happens, I can see that it's there. That's the most useful feature for me in Anaconda.

We use Jupyter notebooks and it was all available in one package when we installed Python.

The interface could be improved. Other solutions, like Visual Studio, have much better UI.

In Visual Studio, we can create projects. I'm not sure it's that kind of feature is possible on Anaconda. In Visual Studio, we can bring a solution and then we can keep multiple projects organized and into it. If there are any libraries, we can have them as well and we can just call them. We can easily link the libraries into the project. This should be possible on Anaconda as well.

I've been using the solution for two years.

The solution is stable.

The scalability of the solution is good. I'm not sure if we'll be increasing usage in the future.

The initial setup was straightforward. We haven't made a deployment; we just use it for learning purposes.

I'd rate the solution seven out of ten. I haven't been using the solution for too long, and I'm still learning things, so I need to explore more before rating it higher.

I use this solution for some of my assignments. Basically, it is used to take data from our database, analyze it, and make predictions.

The most advantageous feature is the logic building.

The Python libraries are all readily available and there is no need to install anything separately.

There are many good things that are pre-built, and including even more of these would be a great benefit to the developer community. It would allow us to try specific models and use cases, then customize them as per a particular activity.

I would like to see the inclusion of some statistical modeling functionality.

Having some examples built-in that we can customize based on the use case, rather than having to build the entire model, would really be an advantage.

Additional support for the visualizations would be an improvement.

The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform.

I have been using Anaconda for about a year.

The stability of the platform is quite good.

I have not tried to scale Anaconda, but we will be working on that shortly. At this time we have between ten and twelve users, and we are looking at how to extend that and still be compliant.

All of our users are technicians.

I have not contacted the technical support directly because to this point, we have relied on help from friends, colleagues, and the community. Most of the problems, we have been able to sort out ourselves.

Prior to Anaconda, we were using SAS for some of our predictive analytics.

The main reason we switched is because of the licensing cost that is incurred for these kinds of specialized software solutions. In addition, functionality is limited to predictive modeling and some specific types of analysis. In Anaconda, there are a lot of other aspects that you can try. 

I found the initial setup to be moderate. It was not too complex nor too easy. We had a couple of people who were working on it and we were able to sort it out with assistance from the community help channels.

It takes between three and four hours to complete the setup entirely.

We implemented this solution ourselves.

The licensing costs for Anaconda are reasonable.

Our team is working on expanding the use of Anaconda. They're doing some research with respect to some of the libraries and modules, trying to do different things with existing datasets. I have been doing some slicing and analysis based on what has already been developed, and we are trying new things now.

My advice for anybody who is implementing this solution is to start with a straightforward deployment. However, if they want to start with deep learning immediately, the functionality is there, but I would recommend the full deployment.

This is a good solution, but there is a little room for improvement.

I would rate this solution an eight out of ten.

My position is master of data and we are a customer of Anaconda. Our primary use case was to find technological solutions to manage our warehouse in conjunction with our customer base. Anaconda enabled me to plot the data on a graph and find the optimal area for where our warehouse should be located.

I mainly used the product for the libraries. 

I hit some contribution issues and attribution problems, so the product could be improved in that area. There's always room for improvement. It's one thing if you have an IT guy with the solution, but there were some cases when it wasn't so simple. There were a lot of typos in the documentation and because we were using the product on-premise, the solution had to be implemented by the IT team here and they had some difficulty fixing problems, particularly from the wall sheet. I think better documentation or a step-by-step guide for installation would help, especially for on-premise users. That would be great.

I haven't used it enough to think about additional features and I didn't hit any roadblocks that made me think about that. It worked well for me. 

I've used it for a couple of months. 

I think it's pretty stable compared to the RStudio solution. There are some good tools which work better and quickly. 

I generally don't use technical support, so I don't have any experience with that.

The setup was quite complex. It was easy to get the whole way through, but we had some issues getting the correct function we needed and getting it properly. Deployment took around ten days to two weeks because our IT guys weren't able to work on it full-time. We didn't use any external help, it was our IT team who did the job. 

I would recommend having a good background so that you know what you're getting into and whether Anaconda is the right solution for you. If you have a strong IT team to support the solution it's a very good tool to work on.

I would rate the solution a seven out of 10. 

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.

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.