Anaconda Reviews

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.

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.

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.

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.

We use Anaconda to develop machine learning models. Use primarily use Scikit-learn and TensorFlow.

The notebook feature is an improvement over RStudio.

I was using this solution for buildings some PoCs, as well as during a hackathon.

The most valuable feature is the Jupyter notebook that allows us to write the Python code, compile it on the fly, and then look at the results.

I found the navigators and the other features to be very good for quick development.

There is a system that is similar to a marketplace where we can search for and install all the tools that we need, and they have been collated in one place.

l began using it because it was open source and it was free and I knew other people who were using it. I just installed it and I got on with my testing. It was very useful for me because I could save my coding and present it to my assessor.  

There are several things that I think are valuable in the product. My first impressions were the product was fairly responsive, sleek and had a beautiful interface that was pleasant to use. It helped me to be able to easily share code between me and my colleagues.  

I had R installed at that time as well. It worked with R as well as Python. R is good for statistics and visualization. I've used R with Tableau as well and for my situation at the time, Anaconda was a bit superior in respect to this integration.  

We primarily use the solution for the data science class. It's used for people to build their data science models.

The solution's most valuable aspects include the repositories, the way we log, the way we install that repository management, and how we can easily integrate the solution with Jupyter Notebook.

The virtual environment is very good.