I use the Anaconda AI Platform for learning and building myself since it's open source, which I really love. It's an easy-to-use and understand platform. It bridges the gap for me and makes my journey into being an ML engineer easier and secure. The initial setup was quite easy for me as I have first-hand knowledge. I've already recommended it to a colleague who wanted to start with Python coding and helped with the installation.

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.

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.