Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and research. Anaconda also provides enterprise-grade security to open-source software through the Premium Repository.
$0
per month
IBM watsonx.ai
Score 8.7 out of 10
N/A
Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a studio spanning the AI lifecycle. Watsonx.ai can be used to train, validate, tune, and deploy generative AI, foundation models, and machine learning capabilities, and build AI applications with less time and data.
I have asked all my juniors to work with Anaconda and Pycharm only, as this is the best combination for now. Coming to use cases: 1. When you have multiple applications using multiple Python variants, it is a really good tool instead of Venv (I never like it). 2. If you have to work on multiple tools and you are someone who needs to work on data analytics, development, and machine learning, this is good. 3. If you have to work with both R and Python, then also this is a good tool, and it provides support for both.
I have built a code accelerator tool for one of the IBM product implementation. Although there was a heavy lifting at the start to train the model on specifics of the packaged solution library and ways of working; the efficacy of the model is astounding. Having said that, watsonx.ai is very well suited for customer service automation, healthcare data analytics, financial fraud detection, and sentiment analysis kind of projects. The Watsonx.ai look and feel is little confusing but I understand over a period of time , it will improve dramatically as well. I do feel that Watsonx.ai has certain limitations from cross-platform deployment flexibility. If an organization is deeply invested in a multi-cloud environment, Watson's integration on other cloud platforms may not be seamless comported to other AI platforms.
Anaconda is a one-stop destination for important data science and programming tools such as Jupyter, Spider, R etc.
Anaconda command prompt gave flexibility to use and install multiple libraries in Python easily.
Jupyter Notebook, a famous Anaconda product is still one of the best and easy to use product for students like me out there who want to practice coding without spending too much money.
I used R Studio for building Machine Learning models, Many times when I tried to run the entire code together the software would crash. It would lead to loss of data and changes I made.
I would love it to provide more low-code or no-code options so we could offer Watsonx to non-developer staff and students instead of ChatGPT or Copilot.
They should have a natural language interface to the AI Assistant analytics so that there is no need to graph these outside Watson.
Similarly, the 30 day limit on conversation data is limiting and drives us to build reporting outsdie IBM watsonx.ai.
It's really good at data processing, but needs to grow more in publishing in a way that a non-programmer can interact with. It also introduces confusion for programmers that are familiar with normal Python processes which are slightly different in Anaconda such as virtualenvs.
I am giving this rating because I have been using this tool since 2017, and I was in college at that time. Initially, I hesitated to use it as I was not very aware of the workings of Python and how difficult it is to manage its dependency from project to project. Anaconda really helped me with that. The first machine-learning model that I deployed on the Live server was with Anaconda only. It was so managed that I only installed libraries from the requirement.txt file, and it started working. There was no need to manually install cuda or tensor flow as it was a very difficult job at that time. Graphical data modeling also provides tools for it, and they can be easily saved to the system and used anywhere.
I needed some time to understand the different parts of the web UI. It was slightly overwhelming in the beginning. However, after some time, it made sense, and I like the UI now. In terms of functionality, there are many useful features that make your life easy, like jumping to a section and giving me a deployment space to deploy my models easily.
Anaconda provides fast support, and a large number of users moderate its online community. This enables any questions you may have to be answered in a timely fashion, regardless of the topic. The fact that it is based in a Python environment only adds to the size of the online community.
I have experience using RStudio oustide of Anaconda. RStudio can be installed via anaconda, but I like to use RStudio separate from Anaconda when I am worin in R. I tend to use Anaconda for python and RStudio for working in R. Although installing libraries and packages can sometimes be tricky with both RStudio and Anaconda, I like installing R packages via RStudio. However, for anything python-related, Anaconda is my go to!
IBM watsonx.ai stands out in the ecosystem of artificial intelligence tools for its combination of flexibility, scalability and the ability to integrate multiple services in a single environment IBM watsonx.ai se destaca no ecossistema de ferramentas de inteligência artificial por sua combinação de flexibilidade, escalabilidade e capacidade de integrar múltiplos serviços em um único ambiente
It has helped our organization to work collectively faster by using Anaconda's collaborative capabilities and adding other collaboration tools over.
By having an easy access and immediate use of libraries, developing times has decreased more than 20 %
There's an enormous data scientist shortage. Since Anaconda is very easy to use, we have to be able to convert several professionals into the data scientist. This is especially true for an economist, and this my case. I convert myself to Data Scientist thanks to my econometrics knowledge applied with Anaconda.
