Anaconda Reviews

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.

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 part of the solution is the virtualization. You can use Python within the virtual environment. It gives us more than the local environment. In there you can do lots of useful things. 

The documentation is excellent and the solution has a very large and active community that supports it.

The solution's support is important and needs to be better. I don't have the last update due to the fact that when I tried to update it I had an error and ran into issues. It's not just me; lots of people in the community don't have the last update. If support was better they may be able to address issues like this faster.

The stability could be improved. Stability is very important because if you develop some product or some program, you want a very, very stable software that you can use for more than two or three years. 

When you install Anaconda for the first time, it's really difficult to update it.

I can't think of any features that are lacking. Overall, it works quite well for me.

I have been using the solution for about two years now.

The stability is difficult to determine. I've heard of many people having issues. And, right now, a lot of people can't deploy the latest update. The stability could be better, in all honesty.

The solution can scale.

I'm a data science student, so I haven't actually had to scale it myself. I know of others who use it and work with it, and they've never had issues.

The documentation is very, very good for this product. Python and Anaconda have very, very big communities, similar to Stack Overflow and GitHub. If you have a problem or you want some answers, or if you have a request for more information on a certain topic, you can easily find exactly what you need.

In the beginning, the initial setup was complex due to the fact that I began with the virtual environment and the virtual environment is very different than the normal environment. With Anaconda it's very different than the normal Python. We use a document to code like JupyterLab. It's not like normal python code. That makes it a bit tricky.

The installation only took a few hours. It wasn't a lengthy process. It's very quick to deploy.

I can't do an update on the solution, so I don't have the latest version. I'm one version behind the latest.

I'm a developer. I work in data science. I work with different data science libraries like Pandas, NumPy, etc., and I use it for analyzing data. Therefore, I'm more of a customer than I am a partner. I don't have a business relationship with the company.

I'd recommend the solution to others.

Overall, I'd rate the solution eight out of ten. It's quite good. It just needs to be more stable and easier to update.

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 use different data science platforms for customer-specific projects. Whatever is being requested by, or is required by the customer, we learn it. Python is one of the technologies that we have a lot of experience with, and it is part of Anaconda.

Our primary use case is analytics. We use Anaconda to build models that predict the probability of an event, or it can be used for classification purposes. There are various uses for this tool.

One of the things that we do is subrogation and I can explain by using the example of a car accident. When an accident happens, you take your car to your insurance company and give them details about what happened. Also, the advisor at a service center will write down relevant information and supply it to the insurance company as well. At this point, the insurance company reimburses expenses for all of the damages that you have incurred. At the same time, they would like to find out if there is any fault that can be attributed to another person. If so, then they want to know whether it is possible to make any kind of recovery from that person or their insurance company.

With thousands of these claims coming into the insurance companies, it is very difficult for somebody to read all of the information and decide whether there is a potential for recovery or not. This is where our application comes into effect. We read all of the data into our software, which is built with Python using Anaconda, and try to gain an understanding of each and every case. This includes many details, even claim history, and we try to assess what the chances are of recovery or what the chances are of subrogation in each case.

This is just an example from one of our several clients. Each customer has different requirements and we customize a solution based on their needs.

The most valuable feature is the set of libraries that are used to support the functionality that we require. We use different libraries for finance and numbers, and we use the scikit-learn library for machine learning. A few of these libraries are very helpful and there is a very long list of them.

I think that the framework can be improved to make it easier for people to discover and use things on their own.

They need a better interface because currently, we have to do everything through coding. It would be nice to have a simple description of what each library is used for and how to use it.

I would like to see additional libraries included to support computer vision and natural language processing. The framework gives us the ability to create them, but having more in place would mean that we would need to do less coding.

Stability is not something that we really consider for this solution. When we are using Anaconda, we have to develop most of the things from scratch. It's a framework, and it is one of the tools that we use so that we do not have to think about dependencies. When I have Anaconda in my environment, I do not have to think about any prerequisites that may be required.

We have not been in contact with technical support to this point.

The initial setup is straightforward and not too difficult.

The length of time required for deployment changes after the first time. If somebody has to build everything then it takes longer. However, once all of the libraries are built, it takes one person perhaps three hours to deploy into production if it is done without interruption.

We have our own team for deploying this solution.

This is a great tool to work with, even if you are starting your career in analytics or another stream like data engineering or data science. This is a tool for everyone because you don't need to think about many things, such as what needs to be installed.

I would rate this solution an eight 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.