Anaconda Business Reviews

I typically use Anaconda Business for personal uses; actually, I use Anaconda machine rather than Anaconda Business.

I use Anaconda Business mainly for development purposes.

I use Anaconda Business for data analytics, as I train AI models using the many libraries that Anaconda provides, particularly with Python language for training purposes.

We appreciate Anaconda Business mainly for its features and offerings.

The scalability of Anaconda Business has been good for our team.

We find the advanced security, governance, and collaborative features for organizations using Python and R particularly useful, as these business plans provided by Anaconda Business have been very helpful.

The prices for Anaconda Business may need improvement.

The usability of Anaconda Business is good, with no complaints about that. However, the pricing should be a little lower for a single person to use, as it might be affordable for an organization, but for my single use, it is difficult.

I've been working in my current field for about two years.

Since the last quarter, we have seen some positive outcomes.

I would advise others looking into using Anaconda Business to consider everything I have found in my research, as everything, including scalability and product features, is time-efficient.

On a scale of one to ten, I rate Anaconda Business a ten.

Anaconda can't handle heavy workloads. From an improvement perspective, I want Anaconda to be able to handle heavy workloads.

For some enterprise versions or wherever there is a need for cloud-based tools to deal with large amounts of data, I feel that it would be good if Anaconda has a partnership or is able to integrate with Databricks.

I have experience with Anaconda for years.

In my company, around 10 to 30 people were using the product.

In my company, I use Databricks 90 percent of the time.

I have not encountered any challenges during the deployment process of Anaconda, especially considering that I haven't worked on heavy data.

My company uses the free version of the tool. There is also a paid version of the tool available.

In terms of development, Anaconda is better than Databricks because computing costs are involved while using the latter tool. If the data is not too large and if a company can work on sample scripts while ensuring that within the organization, everything gets standardized, development can be done on Anaconda, and then users can run production scripts on Databricks because it is popularly used considering the heavy data it can manage.

I have used the product for data engineering and for ML models.

Anaconda's ability to streamline our company's workflow in data analysis has pros and cons attached to it. In terms of pros, Anaconda's advantage over Databricks revolves around the use of system resources. Everything in Databricks is on an online computing basis, where our company uses the product's resources, but our own resources aren't utilized. In our company, we have heavy machines with us, but they aren't used when we use Databricks. I think some small-scale workloads can be handled in Anaconda. In terms of the entire lifecycle, I think Databricks has a lot of advantages over Anaconda. You have features that help you revive old models or deploy your models within the same Databricks. Databricks offers an end-to-end lifecycle over Anaconda.

Working with the integrations of various libraries and tools within Anaconda, I have not faced any issues. Anaconda offers advantages to its users when the workload or data is not much. I am not sure if the paid version of the product is on a computing basis, but if it is, then there is not much of a difference between Anaconda and the other products in the market. As per my understanding, even the enterprise version can be hosted on the company servers, so there are not many costs involved.

I recommend the product to those who plan to use it. The product can be useful in multiple sectors other than the financial sector. In the financial sector, Anaconda can be useful if the workloads are very low, there are many non-priority tasks, and the data is not much used. Issues occur when teams working in collaboration want to use Anaconda and Databricks together. I can use Anaconda for non-heavy tasks. I can go with Databricks for heavy tasks. It would be good if Anaconda and Databricks could have integration capabilities. For computing, you can use Anaconda and the resources from Databricks.

I rate the tool an eight out of ten.

I have been very enthusiastic about artificial intelligence and machine learning since my first year. I started learning Python in my first year and was using a MacBook with the M1 chip, which didn't have native Python support. 

I discovered Anaconda, which developed Python for Mac, so I started using it for Python. Later, I realized its use cases in machine learning and data science.

The best thing is that it provides all the frameworks and makes it easy to create environments for multiple projects using Anaconda.

It is easy for a beginner to learn to use Anaconda. Comparatively, it is easier than using virtual environments or other environments because of the Conda environment. 

However, there are many things in Anaconda that people need to be aware of, so it can be challenging.

A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area. 

Maybe a graphical user interface where we can just input our dataset, and it will handle everything graphically and automatically, could improve Anaconda.

I have been using Anacondas since my second year in college. This year, in 2024, I just graduated with my computer science degree, so it's been about two or three years.

So, I have been using it for two to three years. 

Everything new has some bugs, but it is quite stable for machine learning applications.

In my college, almost 70 to 80 percent of people used Anaconda.

I contacted the support when I was creating a hand gesture product, and some libraries were not working because of the Python version mismatch with the library version. So, I contacted Anaconda support, and they were helpful, replying within a day.

Positive

It is easy to install, set up, and deploy Anaconda.

Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks, which can be expensive. It works on all systems and is not subscription-based.

If you're into machine learning and data science, I would absolutely recommend it because it's essential for those fields. But if you're just exploring and learning Python, it might be too heavy for your computer. 

However, if you're dedicated, I would recommend it.

Overall, I would rate the solution a ten out of ten because it has a lot of functionality available, supports many libraries, and the developers are continually improving it. It suits my needs best.

If I had to go back, I would use Anaconda again.

I use the tool for Jupyter Notebook.

The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.

Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring. 

I have been using the product for five years.

Four to five developers use the product. 

I haven't contacted the tool's support team for any help or questions. I primarily use it for one of its services, and I write my own code within that service, so I haven't felt the need to contact support.

The product is straightforward and self-explanatory. Users can easily follow the next steps provided and install it without much difficulty.

The tool is open-source. 

Some basic tutorials can be found on YouTube, which can help you understand how Anaconda services work. Watching these tutorials can make it easier for someone to use the product. Using it for the first time can be considered at a medium difficulty level, neither too easy nor too difficult. The package management system has greatly improved my development process. I can easily install and incorporate any package I need into my work. I rate the overall product an eight out of ten.

I have been using Anaconda Business for data science practice over the last two years. Anaconda Business serves as my primary tool for data analysis of data science projects. I specifically use Anaconda Business for Jupyter notebooks, where I employ the Python language for predictive modeling and data analysis.

Anaconda Business offers excellent visualization tools including Visual Studio, comprehensive analysis tools, and Python language analysis that significantly helps with building analysis and machine learning models. I have found that Jupyter notebook within Anaconda Business provides the best support for outlier detection and scatter plot work. Anaconda Business has positively impacted my organization by reducing analysis time, which allows me to build machine learning models in a short timeframe.

I am still becoming familiar with Anaconda Business tools after using them for only two years, so I am not utilizing Anaconda Business extensively in data analysis and data science projects yet. I cannot confidently recommend specific improvements at this stage, as I am still in the learning phase with the platform.

I have been working in my current field for the last six years.

In my experience, Anaconda Business is stable.

Anaconda Business scalability is quite useful as compared to other tools.

I have not used customer support for Anaconda Business yet.

For data science, I am using only Anaconda Business and Jupyter notebook, with no other tools.

I do not have experience with pricing, setup cost, and licensing for Anaconda Business, as I am not a decision-taker in my company and these matters are not my concern.

I am not currently familiar with whether I have seen a return on investment from using Anaconda Business.

Before choosing Anaconda Business, I evaluated Power BI as an alternative option.

My advice to others looking into using Anaconda Business is that it offers different tools that are usable for data analysis projects and predictive modeling, making it a great tool to use. I would rate this review a 7.

We use Anaconda for data science model and development, specifically for coding in Python. We use it mainly for forecasting and predicting models within the environment of Anaconda Python.

Anaconda has been very beneficial for our organization. The integration between different utility projects and the overall efficiency for data science and machine learning tasks has been very helpful. It provides a coding environment that saves time in setting up individual tools.

Anaconda has multiple valuable features. It provides a unified platform where you can install Jupyter, Python Spider, and other related tools without needing separate installations. The ability to work on multiple programming languages like Python, R, and Ruby is also significant. One of the best aspects is the community support.

Anaconda consumes a significant amount of processing memory when working on it. This is something that needs improvement as it can impact performance.

I have been using Anaconda for four years.

Anaconda is stable ninety percent of the time. However, there are occasional delays due to high memory consumption.

There is generally no need to scale Anaconda. The tool has minimum system requirements that need to be met for optimal performance.

Anaconda has great community support, and its technical support is also helpful.

Positive

The initial setup of Anaconda is straightforward. One person is enough for the installation and deployment.

Anaconda is cost-saving as it is open-source and the installation is easy. There is no need for a pricing structure for the basic version.

Anaconda does not require a pricing structure, and it is available as an open-source tool. The features of Python, Jupyter, and others are free to use.

I'd rate the solution nine out of ten.

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.

Voice Configuration and Environmental Management Capabilities are the most valuable features. 

If we want to do some big applications, then we need to install a lot of packages. It also takes up a lot of space.

I have been using Anaconda for six years. 

It is a scalable solution. 

The initial setup is straightforward. The deployment took twenty to thirty minutes.

Overall, I rate the solution a nine out of ten.