I use Anaconda for predictions, building models, and incorporating them within the product for specific use cases.

Anaconda is an open-source platform that can integrate numerous other kits and models in one place. It offers effective package management capabilities that are beneficial. By having control over the integrations, I can do them myself without limitations.

There is room for improvement, especially regarding deployment. The process could be streamlined as the number of actions needed to deploy is quite large compared to other tools.

I have been using Anaconda for the past eight or nine years.

The stability of Anaconda is comparable to other tools used in data science. There are always performance issues within data science, but Anaconda is not different from others.

Anaconda does not have scalability restrictions as it depends on the type of machine running it.

I have no experience with Anaconda's technical support.

Neutral

I have used other data science platforms like H2O.ai and Dataiku but prefer Anaconda due to its open-source nature, which allows better control over what I can do.

Anaconda is an open-source tool, so I do not pay anything for it. It is compatible with every tool, regardless of whether it is open source or a paid package.

I have previously used Dataiku, H2O.ai, and Databricks for data science solutions.

The biggest disadvantage is the amount of time needed for deployment to production. My overall experience with Anaconda is positive, and I would rate it an eight out of ten.

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 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.

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. 

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.