The biggest disadvantage is the amount of time needed for deployment to production. My overall experience with Anaconda is positive, and I would rate it an eight out of ten.
If you're into machine learning and data science, I would absolutely recommend it because it's essential for those fields. But if you're just exploring and learning Python, it might be too heavy for your computer. However, if you're dedicated, I would recommend it. Overall, I would rate the solution a ten out of ten because it has a lot of functionality available, supports many libraries, and the developers are continually improving it. It suits my needs best. If I had to go back, I would use Anaconda again.
Consultant - Data Analytics and Reporting at a tech vendor with 51-200 employees
Real User
Top 5
2024-04-03T13:34:08Z
Apr 3, 2024
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.
Cluster Manager - Risk at a financial services firm with 10,001+ employees
Real User
Top 20
2024-03-15T08:16:27Z
Mar 15, 2024
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.
I can't do an update on the solution, so I don't have the latest version. I'm one version behind the latest. I'm a developer. I work in data science. I work with different data science libraries like Pandas, NumPy, etc., and I use it for analyzing data. Therefore, I'm more of a customer than I am a partner. I don't have a business relationship with the company. I'd recommend the solution to others. Overall, I'd rate the solution eight out of ten. It's quite good. It just needs to be more stable and easier to update.
Analytics Analyst at a tech services company with 10,001+ employees
Real User
2020-08-13T08:33:00Z
Aug 13, 2020
I would recommend it to anyone willing to work in data science. This will be a starting place that covers data-wrangling aspects, user relation aspects, and everything. It is a one-stop solution for everything. Anaconda is the main go-to place for analytics. This solution is very handy for almost all data science people. A lot of people I know nowadays use Anaconda. I don't think any other product can even come near Anaconda for data science. I would rate Anaconda a nine out of ten. The long reboot time and once in a while crash are the two things that lack in Anaconda. Apart from that, I don't see any issues with Anaconda.
My only advice to people considering this type of solution is just to use Anaconda. It is a good product. Other products are good as well, but this is one you should try in this category. On a scale of one to ten where one is the worst and ten is the best, I would rate Anaconda in comparison to other products as between nine and ten. It is a very good solution. I will rate it a nine as there is always room for improvement.
Sr PHP Developer at a manufacturing company with 10,001+ employees
Real User
2019-12-19T06:32:00Z
Dec 19, 2019
My advice to anybody who is researching this tool is to download and install it, then start exploring the different tools that are available. I suggest starting with Jupyter because it is easy to figure out, then move to Python tasks. This is a good tool that is quick to deploy with and pretty easy to use. I have not fully explored it yet, so I can only give it an average rating. I would rate this solution a five out of ten.
Senior Software Design Engineer at a financial services firm with 5,001-10,000 employees
Real User
2019-12-16T08:14:00Z
Dec 16, 2019
I'd rate the solution seven out of ten. I haven't been using the solution for too long, and I'm still learning things, so I need to explore more before rating it higher.
Head - Data Science (Senior Program Manager) at a tech services company with 51-200 employees
Real User
2019-12-16T08:14:00Z
Dec 16, 2019
This is a great tool to work with, even if you are starting your career in analytics or another stream like data engineering or data science. This is a tool for everyone because you don't need to think about many things, such as what needs to be installed. I would rate this solution an eight out of ten.
Solution Architect/Technical Manager - Business Intelligence at a tech services company with 5,001-10,000 employees
Real User
2019-12-15T05:58:00Z
Dec 15, 2019
Our team is working on expanding the use of Anaconda. They're doing some research with respect to some of the libraries and modules, trying to do different things with existing datasets. I have been doing some slicing and analysis based on what has already been developed, and we are trying new things now. My advice for anybody who is implementing this solution is to start with a straightforward deployment. However, if they want to start with deep learning immediately, the functionality is there, but I would recommend the full deployment. This is a good solution, but there is a little room for improvement. I would rate this solution an eight out of ten.
Data Scientist Chapter Lead, Workflow & Automation at ANZ Banking Group
Real User
2019-12-12T07:48:00Z
Dec 12, 2019
This is a product that I encourage people to use. This is a good solution, but I would like to see multi-language support. I would rate this solution a seven out of ten.
Senior tech architect at a computer software company with 1,001-5,000 employees
Real User
2019-12-11T05:40:00Z
Dec 11, 2019
We use various deployment models but mostly work with hybrid models. We don't rely on Anaconda's deployment defense a lot. We use the solution mainly for Python distribution and deployment monitoring internals. We deploy in Docker and scale it. It's one of the best tools available out there. If you have to get started very quickly, it's great. Almost everything is ready for you to use. I think it's a wonderful tool for developers to get started with. I'd rate the solution nine out of ten.
Master Data at a energy/utilities company with 1,001-5,000 employees
Real User
2019-12-09T10:58:00Z
Dec 9, 2019
I would recommend having a good background so that you know what you're getting into and whether Anaconda is the right solution for you. If you have a strong IT team to support the solution it's a very good tool to work on. I would rate the solution a seven out of 10.
Anaconda Business provides a comprehensive platform for data science applications, integrating extensive libraries and seamless Python and R compatibility, enhancing developer productivity.Anaconda Business offers data science professionals a platform combining extensive library support with pre-built models and seamless integration of Python and R environments. With features like a user-friendly interface and integrated Jupyter Notebook, it facilitates real-time code execution and debugging....
The biggest disadvantage is the amount of time needed for deployment to production. My overall experience with Anaconda is positive, and I would rate it an eight out of ten.
I'd rate the solution nine out of ten.
If you're into machine learning and data science, I would absolutely recommend it because it's essential for those fields. But if you're just exploring and learning Python, it might be too heavy for your computer. However, if you're dedicated, I would recommend it. Overall, I would rate the solution a ten out of ten because it has a lot of functionality available, supports many libraries, and the developers are continually improving it. It suits my needs best. If I had to go back, I would use Anaconda again.
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.
Overall, I rate the solution a nine out of ten.
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.
I can't do an update on the solution, so I don't have the latest version. I'm one version behind the latest. I'm a developer. I work in data science. I work with different data science libraries like Pandas, NumPy, etc., and I use it for analyzing data. Therefore, I'm more of a customer than I am a partner. I don't have a business relationship with the company. I'd recommend the solution to others. Overall, I'd rate the solution eight out of ten. It's quite good. It just needs to be more stable and easier to update.
I would recommend it to anyone willing to work in data science. This will be a starting place that covers data-wrangling aspects, user relation aspects, and everything. It is a one-stop solution for everything. Anaconda is the main go-to place for analytics. This solution is very handy for almost all data science people. A lot of people I know nowadays use Anaconda. I don't think any other product can even come near Anaconda for data science. I would rate Anaconda a nine out of ten. The long reboot time and once in a while crash are the two things that lack in Anaconda. Apart from that, I don't see any issues with Anaconda.
My only advice to people considering this type of solution is just to use Anaconda. It is a good product. Other products are good as well, but this is one you should try in this category. On a scale of one to ten where one is the worst and ten is the best, I would rate Anaconda in comparison to other products as between nine and ten. It is a very good solution. I will rate it a nine as there is always room for improvement.
We use the on-premises deployment model. I'd rate the solution seven out of ten.
My advice to anybody who is researching this tool is to download and install it, then start exploring the different tools that are available. I suggest starting with Jupyter because it is easy to figure out, then move to Python tasks. This is a good tool that is quick to deploy with and pretty easy to use. I have not fully explored it yet, so I can only give it an average rating. I would rate this solution a five out of ten.
I'd rate the solution seven out of ten. I haven't been using the solution for too long, and I'm still learning things, so I need to explore more before rating it higher.
This is a great tool to work with, even if you are starting your career in analytics or another stream like data engineering or data science. This is a tool for everyone because you don't need to think about many things, such as what needs to be installed. I would rate this solution an eight out of ten.
Our team is working on expanding the use of Anaconda. They're doing some research with respect to some of the libraries and modules, trying to do different things with existing datasets. I have been doing some slicing and analysis based on what has already been developed, and we are trying new things now. My advice for anybody who is implementing this solution is to start with a straightforward deployment. However, if they want to start with deep learning immediately, the functionality is there, but I would recommend the full deployment. This is a good solution, but there is a little room for improvement. I would rate this solution an eight out of ten.
This is a product that I encourage people to use. This is a good solution, but I would like to see multi-language support. I would rate this solution a seven out of ten.
We use various deployment models but mostly work with hybrid models. We don't rely on Anaconda's deployment defense a lot. We use the solution mainly for Python distribution and deployment monitoring internals. We deploy in Docker and scale it. It's one of the best tools available out there. If you have to get started very quickly, it's great. Almost everything is ready for you to use. I think it's a wonderful tool for developers to get started with. I'd rate the solution nine out of ten.
I would recommend having a good background so that you know what you're getting into and whether Anaconda is the right solution for you. If you have a strong IT team to support the solution it's a very good tool to work on. I would rate the solution a seven out of 10.