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Unified platform for efficient coding and machine learning
What is our primary use case?
How has it helped my organization?
Anaconda has been very beneficial for our organization. The integration between different utility projects and the overall efficiency for data science and machine learning tasks has been very helpful. It provides a coding environment that saves time in setting up individual tools.
What is most valuable?
Anaconda has multiple valuable features. It provides a unified platform where you can install Jupyter, Python Spider, and other related tools without needing separate installations. The ability to work on multiple programming languages like Python, R, and Ruby is also significant. One of the best aspects is the community support.
What needs improvement?
Anaconda consumes a significant amount of processing memory when working on it. This is something that needs improvement as it can impact performance.
For how long have I used the solution?
I have been using Anaconda for four years.
What do I think about the stability of the solution?
Anaconda is stable ninety percent of the time. However, there are occasional delays due to high memory consumption.
What do I think about the scalability of the solution?
There is generally no need to scale Anaconda. The tool has minimum system requirements that need to be met for optimal performance.
How are customer service and support?
Anaconda has great community support, and its technical support is also helpful.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup of Anaconda is straightforward. One person is enough for the installation and deployment.
What was our ROI?
Anaconda is cost-saving as it is open-source and the installation is easy. There is no need for a pricing structure for the basic version.
What's my experience with pricing, setup cost, and licensing?
Anaconda does not require a pricing structure, and it is available as an open-source tool. The features of Python, Jupyter, and others are free to use.
What other advice do I have?
I'd rate the solution nine out of ten.
Best Open Source Tool
A cloud-based solution that can be accessed from anywhere
What is our primary use case?
I use the tool for Jupyter Notebook.
What is most valuable?
The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.
What needs improvement?
Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring.
For how long have I used the solution?
I have been using the product for five years.
What do I think about the scalability of the solution?
Four to five developers use the product.
How are customer service and support?
I haven't contacted the tool's support team for any help or questions. I primarily use it for one of its services, and I write my own code within that service, so I haven't felt the need to contact support.
How was the initial setup?
The product is straightforward and self-explanatory. Users can easily follow the next steps provided and install it without much difficulty.
What's my experience with pricing, setup cost, and licensing?
The tool is open-source.
What other advice do I have?
Some basic tutorials can be found on YouTube, which can help you understand how Anaconda services work. Watching these tutorials can make it easier for someone to use the product. Using it for the first time can be considered at a medium difficulty level, neither too easy nor too difficult. The package management system has greatly improved my development process. I can easily install and incorporate any package I need into my work. I rate the overall product an eight out of ten.
Simplifies package and environment management across operating systems
What is our primary use case?
What is most valuable?
Voice Configuration and Environmental Management Capabilities are the most valuable features.
What needs improvement?
If we want to do some big applications, then we need to install a lot of packages. It also takes up a lot of space.
For how long have I used the solution?
I have been using Anaconda for six years.
What do I think about the scalability of the solution?
It is a scalable solution.
How was the initial setup?
The initial setup is straightforward. The deployment took twenty to thirty minutes.
What other advice do I have?
Overall, I rate the solution a nine out of ten.
Offers free version and is helpful to handle small-scale workloads
What needs improvement?
Anaconda can't handle heavy workloads. From an improvement perspective, I want Anaconda to be able to handle heavy workloads.
For some enterprise versions or wherever there is a need for cloud-based tools to deal with large amounts of data, I feel that it would be good if Anaconda has a partnership or is able to integrate with Databricks.
For how long have I used the solution?
I have experience with Anaconda for years.
What do I think about the scalability of the solution?
In my company, around 10 to 30 people were using the product.
Which solution did I use previously and why did I switch?
In my company, I use Databricks 90 percent of the time.
How was the initial setup?
I have not encountered any challenges during the deployment process of Anaconda, especially considering that I haven't worked on heavy data.
What's my experience with pricing, setup cost, and licensing?
My company uses the free version of the tool. There is also a paid version of the tool available.
Which other solutions did I evaluate?
In terms of development, Anaconda is better than Databricks because computing costs are involved while using the latter tool. If the data is not too large and if a company can work on sample scripts while ensuring that within the organization, everything gets standardized, development can be done on Anaconda, and then users can run production scripts on Databricks because it is popularly used considering the heavy data it can manage.
What other advice do I have?
I have used the product for data engineering and for ML models.
Anaconda's ability to streamline our company's workflow in data analysis has pros and cons attached to it. In terms of pros, Anaconda's advantage over Databricks revolves around the use of system resources. Everything in Databricks is on an online computing basis, where our company uses the product's resources, but our own resources aren't utilized. In our company, we have heavy machines with us, but they aren't used when we use Databricks. I think some small-scale workloads can be handled in Anaconda. In terms of the entire lifecycle, I think Databricks has a lot of advantages over Anaconda. You have features that help you revive old models or deploy your models within the same Databricks. Databricks offers an end-to-end lifecycle over Anaconda.
Working with the integrations of various libraries and tools within Anaconda, I have not faced any issues. Anaconda offers advantages to its users when the workload or data is not much. I am not sure if the paid version of the product is on a computing basis, but if it is, then there is not much of a difference between Anaconda and the other products in the market. As per my understanding, even the enterprise version can be hosted on the company servers, so there are not many costs involved.
I recommend the product to those who plan to use it. The product can be useful in multiple sectors other than the financial sector. In the financial sector, Anaconda can be useful if the workloads are very low, there are many non-priority tasks, and the data is not much used. Issues occur when teams working in collaboration want to use Anaconda and Databricks together. I can use Anaconda for non-heavy tasks. I can go with Databricks for heavy tasks. It would be good if Anaconda and Databricks could have integration capabilities. For computing, you can use Anaconda and the resources from Databricks.
I rate the tool an eight out of ten.
Complete software for data science development
Best software for Python and R programming.
Anaconda my ML DL starter
It's easy to implement solutions in anaconda. The latest feature that I checked recently is Anaconda is now available over clouds. Anaconda comes with CONDA which is very helpful to manage multiple environment.
Anaconda Review
2)supply various libraries pandas,numpy and various data analysis libraries and tools.
3)easier with installation and updation of packages.
4)supports version control system
5) helps in integration with tools such as pycharm and vs. code.
2) It's a great tool
It supports libraries such as numpy, pandas, matplotlib, and sci-kit learn.