Great for Learning, Needs a Friendlier Interface
What do you like best about the product?
I find the Anaconda AI Platform very easy to use for data science workflows, which is great for both learning and experimenting. Its enterprise pricing plans are quite affordable and ideal for my needs. I appreciate that it has integrated tooling, notebooks, package managers, interpreters, and all the main libraries already in place, making it very useful for building workflows and practicing skills. It's also accessible for beginners and students who are learning data science and AI.
What do you dislike about the product?
Yes. The interface could be made more user friendly because right now it's a bit hard to get used to at first. And, also, it's very heavy and resource intensive, I would say. And the free tier is limited as well. So that could be improved. Those problems could be solved.
What problems is the product solving and how is that benefiting you?
I use Anaconda AI Platform to learn Python and improve my coding skills. It solves the problem of experimentation and helps me build small systems for my data science lessons. It's easy to use for learning data science workflows.
Data analysis workflows have become faster and now support collaborative modeling and NLP projects
What is our primary use case?
I have been using Anaconda Business for a couple of years since 2022 when I started data analysis, utilizing it for Python programming to query and analyze my data.
My main use cases for Anaconda Business include querying data, analyzing data, building machine learning models, and writing academic projects to create insights from data and obtain findings.
A specific project where Anaconda Business helped me achieve my goals involved analyzing data, querying large datasets, and cleaning the data. I utilized Python programming to run my data through the Python extension and employed it for Natural Language Processing as well.
Another important use case with Anaconda Business involved addressing challenges while writing a project by using Anaconda's Jupyter Notebook to query, analyze, and clean data while ensuring I could run it through some models and obtain findings.
How has it helped my organization?
Anaconda Business has positively impacted my organization by easing the burden of querying large datasets that would otherwise slow down our work when using Excel.
Since switching to Anaconda Business, I have improved productivity by around 80%, saving time in data crunching and exploration.
What is most valuable?
The best features Anaconda Business offers include its various tools for analysis, including Jupyter Notebook, which stands out to me for being able to upload data easily and perform data analysis and visualization.
Anaconda Business supports Python and non-Python programming, provides access to multiple libraries, and allows for collaboration and deployment. My colleague and I used Jupyter Notebook for our collaborative work.
What needs improvement?
I believe Anaconda Business can be improved in terms of performance and speed, particularly regarding the installation process and efficiency to avoid system freezing.
For how long have I used the solution?
I have been using Anaconda Business for a couple of years since 2022 when I started data analysis, utilizing it for Python programming to query and analyze my data.
What do I think about the stability of the solution?
Anaconda Business is stable to an extent, but it sometimes crashes on systems with insufficient RAM, leading me to switch to Google Cloud for Jupyter Notebook usage at times.
How are customer service and support?
I have reached out to their support once, and I found their response was prompt and satisfactory.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Before using Anaconda Business, I used the Python console but switched to Jupyter Notebook in Anaconda for its user-friendly interface that allows me to see my work clearly.
What was our ROI?
I have seen a return on investment from using Anaconda Business as I use it for querying data and driving informed decisions, which has helped me earn money for various projects.
Which other solutions did I evaluate?
Before choosing Anaconda Business, I evaluated R for data visualizations.
What other advice do I have?
My advice for others looking into using Anaconda Business is to have a good laptop to ensure it runs smoothly without hanging. I would rate this product an 8 overall.
Effortless Python Environment Management
What do you like best about the product?
I find it very approachable, which is its greatest advantage. I use it to install packages like Pandas, Numpy, and Scikit-Learn for my Python projects. It helps me maintain a good and organized work environment.
What do you dislike about the product?
The installation process takes up more time and requires more space on your system. The interface also appears quite outdated when compared to others, and at times, the software can be slow.
What problems is the product solving and how is that benefiting you?
I use this tool for data analysis and building machine learning models in the field of education management. I work in this environment every day.
Anaconda: A reliable and secure platform used for AI workflows
What do you like best about the product?
As a AI developer I have used Anaconda for some of my projects and the feature I most like about it are:-
1) Good for projects with shared environment
2) Bundled important python libraries for AI development and Data science which really makes it easy to use
3)Strong open source community to clear doubts
4) Interactive platform for user support and new updates along with the forum
5) Integration with jupyter lab makes it more useful for AI developers
What do you dislike about the product?
The features I dislike are:-
1) It uses too much resources for some project which makes it hard to use on local machine
2) Bundled package are not latest sometimes. which makes it difficult to use new features
What problems is the product solving and how is that benefiting you?
The problem Anaconda AI platform is solving for me is
1) It helps in package managment in team projects.
2) Its secure and scalable.
3) Covinent for installing dependency with out worrying about version mismatch in project are mutually dependent packages
Great for Beginners, Needs Better Optimization
What do you like best about the product?
I find Anaconda AI Platform really helpful for machine learning and deep learning projects because it has a built-in interpreter with most useful packages like numpy, pandas, scikit-learn, PyTorch, and TensorFlow, so I don't have to manually install them. It also has all the IDEs accessible from its platform, making it easier to access, manage, and use. Installing new Python packages in the interpreter is easy through the Anaconda terminal. I like how the user interface is easy to navigate, which is very convenient for a beginner coder like me. This helps me as a beginner in the machine learning and deep learning domain to use different Python packages and access a variety of different IDEs. The initial setup was also very easy.
What do you dislike about the product?
I don't like that it takes too long to load sometimes, and installing new packages in Anaconda terminal may need different input commands than regular Python, which can be confusing for some.
What problems is the product solving and how is that benefiting you?
Anaconda AI Platform simplifies my work by having essential packages like NumPy and TensorFlow pre-installed, making IDEs easily accessible, and offering a user-friendly interface that's convenient for beginners.
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.
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.
Empowers Team Collaboration with Robust Data Tools and AI Assistance
What do you like best about the product?
I love this platform for its powerful and robust tools to synthesize data to a more reasonable output that supports active decision making. Its interface allows co-ordination and co-operation of teams to efficiently collaborate on projects. Though there are lots to say about this platform, I feel that it is worth mentioning about the Anaconda AI Assistant that allows us to create a basic template from where we can customize further to make a complete program is really commendable. I really love its capability to export and run the chat in notebooks.
What do you dislike about the product?
I love most part of this platform but the price of purchasing separate packages can be high. I also would say that lots of earing is really necessary to fully explore its capacity.
What problems is the product solving and how is that benefiting you?
This platform greatly assist in improving efficiency of projects. Its ability to solve critical issues in testing phase is really outstanding.
A must-have tool for a engineering student
What do you like best about the product?
I really appreciate the Anaconda Navigator interface. It makes it extremely easy to launch applications like Jupyter Notebook and VS Code without using the command line. As a student it is perfect for managing libraries and environments visually. I have used it for all my engineering studies.
What do you dislike about the product?
Sometimes the software can be a bit heavy and slow to start up.
What problems is the product solving and how is that benefiting you?
It simplifies the management of Python packages and dependencies. It allows me to create isolated environments for different projects, avoiding conflicts between different versions of libraries.
