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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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? Review collected by and hosted on G2.com.

I primarily use the Anaconda AI Platform for data analysis, machine learning experimentation, and Python environment management. I really appreciate how it helps create and manage isolated environments for different projects, ensuring consistent package versions and reducing dependency conflicts. I like its simplicity and reliability in managing Python environments and packages, making it straightforward to create isolated environments and significantly reducing dependency conflicts. The overall stability and maturity of the platform are impressive, and it feels well-maintained and reliable, especially for long-running projects where environment consistency matters. I also find the documentation and community support very helpful as they usually provide clear guidance or solutions when issues arise, reducing downtime. The initial setup was straightforward and smooth, with simple installation and minimal configuration required. Review collected by and hosted on G2.com.

The platform can feel resource-heavy, especially on systems with limited hardware. Also, conda's dependency resolution can sometimes be slow for large environments. Improving performance and speeding up environment setup would make the experience even better. Review collected by and hosted on G2.com.

I appreciate the Anaconda AI Platform's easy-to-use interface, which significantly enhances my data analytics work. The integration of its AI system within Jupyter Notebooks is particularly useful and makes my tasks more efficient. Additionally, the initial setup of the Anaconda AI Platform was very easy, which made transitioning to this platform smooth and hassle-free. The overall user experience is positive enough that I would highly recommend Anaconda AI Platform to friends or colleagues, as reflected in my high recommendation score of 10. Review collected by and hosted on G2.com.

I dislike the loading time of the main page for the software application, which can be inconvenient. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The Anaconda AI Platform enables AI development that is both effective and convenient. It can seamlessly integrate with machine learning frameworks that are popular with data scientists and enables streamlined setting-up and managing of complex data science projects, allowing users to focus more on developing models, rather than managing the underlying system configuration. Review collected by and hosted on G2.com.

I don't like that the Anaconda AI Platform can be overwhelming sometimes, especially to people who haven't used the software before. There is a lot of content to learn, and not everything feels very "intuitive." When working with large datasets, I find using the platform can be slower than I prefer, which can impact productivity. Review collected by and hosted on G2.com.

Anaconda AI Platform provides a complete environment for managing data science and AI workflows. Its package management and environment handling are seamless, making it easy to install, update, and manage libraries without dependency conflicts. I especially value the large ecosystem of pre-built packages, Jupyter integration, and the ability to quickly prototype, test, and deploy models. It saves a lot of setup time and improves productivity, particularly for Python-based AI development. Review collected by and hosted on G2.com.

While Anaconda is excellent, it can sometimes feel heavy—especially in terms of storage space and environment size. Package downloads can be slow, and certain updates may break compatibility with older environments. The enterprise edition could also benefit from more streamlined integration with cloud platforms and better optimization for large-scale distributed workloads. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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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. Review collected by and hosted on G2.com.

It might be cool to automatically suggest the user restart the kernel when updating/replacing files related to the codebase of the current project. Review collected by and hosted on G2.com.

Honestly, what I find most brilliant about the Anaconda AI Platform is that it just eliminates the "dependency hell" that usually plagues Python development.

If you’ve ever tried to manually install a dozen machine learning libraries only to have them all break because one version of NumPy didn’t agree with another, you’ll know exactly why Anaconda is such a relief. It handles all that messy wiring for you.

Here is a breakdown of what makes it stand out, from a "day-to-day use" (I use at work every week) perspective:

1. The "Batteries Included" Philosophy

The best bit is definitely the convenience of implementation. You download one installer, and suddenly you have everything you need: Python, Jupyter Notebooks, pandas, scikit-learn and hundreds of other data science packages. You don't have to spend your first afternoon hunting down libraries; you can just open a notebook and start coding immediately. It feels like moving into a furnished flat rather than an empty house.

2. Lifesaver

While pip is great, the conda package manager is the real hero here.

Safe Experimentation: It lets you create isolated "environments" for every project. You can have one environment with an old version of TensorFlow for a legacy project and another with the cutting-edge PyTorch version for something new, and they won't interfere with each other.

Non-Python Libraries: Unlike standard Python tools, Conda can install non-Python dependencies (like C libraries) that data science packages often rely on. It saves you from having to compile things from source, which can be a nightmare on Windows.

3. Running AI Locally (Cool Stuff)

Recently, they’ve added features like the AI Navigator, which allows you to download and run Large Language Models (LLMs) directly on your own laptop. This is massive for privacy. You can experiment with AI assistants without sending your data to the cloud. It turns your local machine into a private AI lab, which is incredibly empowering.

4. Agnostic and Stable

It works almost exactly the same whether you are on Windows, macOS, or Linux. This consistency is underrated but vital if you are working in a team where everyone uses different operating systems. You know that if it runs in your Anaconda environment, it’s likely to run in theirs too.

In short, I like it because it lets you focus on the maths and the code rather than the configuration. It removes the friction between having an idea and testing it out.

Note: For customer support, I have yet to experience any of it, thus I cannot comment much about it. Review collected by and hosted on G2.com.

Disclaimer: This is not anything about "dislike". It's just an experience/observation over the years. Hope it helps.

The full Anaconda installer is huge (gigabytes). It installs hundreds of packages you will likely never use. It feels a bit like buying a whole supermarket just because you wanted a pint of milk.

Perhaps this is why many of us switch to Miniconda (the stripped-down version) eventually. Review collected by and hosted on G2.com.