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.
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Effortless Dependency Management and Quick Setup
What do you like best about the product?
I use Anaconda AI Platform for a research project to manage packages with dependency conflicts. I find that conda, in particular, helps me isolate certain packages in their compatible Python versions, which makes it extremely easy and reproducible. I also appreciate using Anaconda environments on Lightning AI because the ease of use and compatibility with GPUs out of the box stand out to me. It saves me a lot of time, especially on cloud environments where I would have to recompile CPython every time I use a new VM. Switching from CPython to Anaconda was a great upgrade, and I found that setting up Anaconda AI Platform was seamless, much easier than compiling my own Python.
What do you dislike about the product?
I haven't explored it too much so I don't think I can answer that properly, but here's something that was a pain point: Wouldn't it be better if pip packages were somehow integrated into conda?
What problems is the product solving and how is that benefiting you?
I use Anaconda AI Platform to manage package dependency conflicts, making package management easy and reproducible. It saves me time by avoiding manual compilation of Python versions and is seamless in setup. I appreciate the GPU compatibility and ease of use in cloud environments.
Reliable, Secure, and User-Friendly Data Science Platform
What do you like best about the product?
I really like the reliability and simplicity of Anaconda AI Platform in managing complex data science projects. The ability to create, isolate, and reproduce environments has significantly reduced bottlenecks for me. I also appreciate the curated package ecosystem and the built-in security scanning, which provide confidence when using open-source libraries. The platform's reliability ensures environments behave consistently across various phases, and its simplicity lowers the barrier for onboarding and accelerates project startups. The security feature reduces the risk associated with using open-source packages, giving visibility into known vulnerabilities and ensuring that libraries come from trusted, vetted sources. Additionally, the initial setup was really straightforward.
What do you dislike about the product?
Environment resolution and package installation can occasionally be slow, particularly for complex environments with many dependencies. Clear documentation and guided workflows for deployment integrations would help reduce start/set-up time and improve discoverability.
What problems is the product solving and how is that benefiting you?
I use Anaconda AI Platform to manage data science workflows, handling environment management and dependency conflicts. It simplifies onboarding, speeds up project startups, and reduces risks with open-source packages through built-in security. Reliability and a curated package ecosystem help maintain consistent environments.
Diverse and Easy to Use, Despite Initial Obstacles
What do you like best about the product?
I find the Anaconda AI Platform very versatile, and I appreciate that you can use many different tools. I particularly like using Jupyter Notebook or Spyder, as errors are displayed and explained. Additionally, I find the usage quite straightforward and easy.
What do you dislike about the product?
At the beginning, the download was a bit confusing, and I didn't really understand which tool I should use in Anaconda. It could be clearer and usable for more things.
What problems is the product solving and how is that benefiting you?
I use the Anaconda AI Platform in lectures to solve programming tasks and build programs. The errors are explained, which makes the usage simple and manageable, and I appreciate the variety of tools, especially Jupyter Notebook and Spyder.
practicality for data science projects
What do you like best about the product?
It greatly helps with data simplification, library updates, and even third-party packages.
What do you dislike about the product?
mainly the high consumption of resources with limited space generates slow performance
What problems is the product solving and how is that benefiting you?
the complexity in the manual installation of libraries for projects
Effortless Setup, Perfect for Machine Learning
What do you like best about the product?
I really appreciate how Anaconda AI Platform is very good at setting up the environments for you. Also, the UX is very nice, making it a pleasant experience to use. The initial setup was very easy, so much that if you just see someone do it, you could do it yourself.
What do you dislike about the product?
I don't like it not connecting to my localhost.but I think that was a me problem
What problems is the product solving and how is that benefiting you?
I use the Anaconda AI Platform to practice Python for machine learning, and it simplifies environment setup with a very nice UX.
Data analysis has become faster and supports predictive models but still offers more to learn
What is our primary use case?
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been working in my current field for the last six years.
What do I think about the stability of the solution?
In my experience, Anaconda Business is stable.
What do I think about the scalability of the solution?
Anaconda Business scalability is quite useful as compared to other tools.
How are customer service and support?
I have not used customer support for Anaconda Business yet.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
For data science, I am using only Anaconda Business and Jupyter notebook, with no other tools.
How was the initial setup?
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.
What was our ROI?
I am not currently familiar with whether I have seen a return on investment from using Anaconda Business.
Which other solutions did I evaluate?
Before choosing Anaconda Business, I evaluated Power BI as an alternative option.
What other advice do I have?
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.
Anaconda: Empowering Analysts with Real-Time Insights
What do you like best about the product?
I use the Anaconda AI Platform for learning and testing purposes, and it helps me create KPI dashboards and reports. I'm really happy and grateful for how it supports me in making charts and analyzing data, especially for LinkedIn. It's very useful for creating dashboard charts and handling heavy Excel sheets without hanging, showing real-time results without needing extra software. I also appreciate that I can connect easily with most databases. The platform's easy setup, without blocking services on Windows, is a major plus. Using Anaconda is a game-changer for learning new things and exploring opportunities. It's very easy to install, and I'm happy with the platform overall.
What do you dislike about the product?
No
What problems is the product solving and how is that benefiting you?
I use Anaconda AI Platform to handle heavy data without hanging, create KPIs easily, and explore new learning opportunities. It provides real-time results and connects with databases like SQL, making it easier for me as an analyst and aspiring data scientist.
Secure, Verified Python Packages for Reliable Model Building
What do you like best about the product?
The platform gives us access to pre-validated and verified Python package distributions, which is crucial for building our fraud and risk models on secure and auditable dependencies.
What do you dislike about the product?
Integrating logging with external monitoring systems can occasionally demand more detailed manual configuration than I would prefer.
What problems is the product solving and how is that benefiting you?
The standardization of our development environments has drastically reduced the time spent resolving dependency conflicts, allowing me to focus more hours on optimizing algorithms.
Convenient cloud solution for Python development
What do you like best about the product?
I like the Anaconda AI Platform for its excellent support and the ability to connect to Anaconda Cloud from any device. I also appreciate the ability to easily transfer files to and from the cloud from any device. The paid version of Anaconda AI allows me to work with data sources on the internet without restrictions, which is extremely important to me. I also find that Anaconda's cloud solution is much more convenient than using a local Python development environment. The initial setup was straightforward, and I handled it easily. Moreover, I am so satisfied with the user experience that I rated the likelihood of recommending the Anaconda AI Platform to a friend or colleague as 10 out of 10.
What do you dislike about the product?
It might be cool to automatically suggest the user restart the kernel when updating/replacing files related to the codebase of the current project.
What problems is the product solving and how is that benefiting you?
I use the Anaconda AI Platform for developing analytical applications in Python and debugging them, solving the problem of code deployment and working with internet sources without restrictions. The platform offers good support and the ability to work with Anaconda Cloud from any device.
All-in-One Data Science Toolkit with Seamless Environment Management
What do you like best about the product?
The batteries included approach is the biggest advantage. I find it very hepful that it pre-installs the most critical data science libraries (Pandas, NumPy, Scikit-learn, Matplotlib) without needing to fight with dependency conflicts manually. The Anaconda Navigator is great for managing separate environment, Eg- I can keep my deep learning environment separate from my standard stock analysis environment.
What do you dislike about the product?
It can be quite heavy on system resources. The base installation consumes a significant amount of disk space. Also the Navigator GUI can sometimes feel sluggish to load on older hardware. The 'Solver' (used for resolving package dependencies) can occasionally be slow when installing new packages.
What problems is the product solving and how is that benefiting you?
I use it primarily for financial data analysis and predictive modeling for the stock market. It solves the problem of 'Environment Hell' by allowing me to quickly spin up isolated Python environments for different projects (e.g., one for Kaggle competitions, one for analyzing NSE stock data). It simplifies the workflow so I can focus on the algorithms rather than the setup.
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