Anaconda Business and Amazon SageMaker are competing products in the data science solutions category. Users indicate a preference for Anaconda Business due to its open-source nature, cost-effectiveness, and ease of deployment, giving it an edge over Amazon SageMaker, which is often seen as more comprehensive but costly.
Features: Anaconda Business provides a unified platform with essential libraries and tools for data science, supporting multiple programming languages and community-backed resources. Its package management system allows for easy environment setup and includes integrated tools like Jupyter Notebook. Amazon SageMaker offers extensive cloud-based machine learning capabilities, such as model deployment, monitoring, and integration options. Its use of pre-built models and task automation particularly benefits enterprise users.
Room for Improvement: Anaconda Business could enhance its handling of heavy workloads, improve the aesthetic of its user interface, and expand package offerings to better meet enterprise needs. Streamlining update processes and building stronger promotional efforts could also help. Amazon SageMaker is often criticized for its high pricing and complexity, with suggestions for improved documentation, simpler integration, a more user-friendly interface, and better cost-management strategies.
Ease of Deployment and Customer Service: Anaconda Business is adaptable to on-premises and hybrid cloud environments, and its strong community support decreases reliance on formal customer service. Users find Anaconda's documentation and resources beneficial for deployment. Amazon SageMaker focuses on public cloud deployment, often assuming familiarity with AWS. It benefits users with existing AWS infrastructure but requires expertise to use it effectively.
Pricing and ROI: Anaconda Business, leveraging its open-source approach, is cost-effective, offering significant savings and high ROI through time efficiency and reduced operational errors. It suits those seeking affordable data science solutions. Conversely, Amazon SageMaker's pricing can lead to high costs due to its complexity, yet offers valuable features that can justify the expense for businesses already using AWS infrastructure. Its ROI is linked to advanced data capabilities despite the need for careful resource management.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
Everyone being able to work smoothly without unnecessary delays.
I have seen a return on investment; specifically, when we talk about efficiency, it's both time-saving and money-saving.
I have seen a return on investment with time saved by 50% and less downtime, allowing the team to deliver projects faster with fewer errors.
The technical support from AWS is excellent.
The support is very good with well-trained engineers.
The response time is generally swift, usually within seven to eight hours.
Anaconda Business customer support is very active with a quick response time.
Overall, support was reliable when we needed it, just not super-fast every single time.
The customer support for Anaconda Business provides a better approach.
The availability of GPU instances can be a challenge, requiring proper planning.
It works very well with large data sets from one terabyte to fifty terabytes.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
As more environments or users get added, it still runs smoothly without major slowdowns.
Anaconda Business scales very well because it is built around centralized environment and package management.
Anaconda does not have scalability restrictions as it depends on the type of machine running it.
There are issues, but they are easily detectable and fixable, with smooth error handling.
The product has been stable and scalable.
I rate the stability of Amazon SageMaker between seven and eight.
Earlier, setting up or troubleshooting conflicts could take anywhere from thirty minutes to an hour, but now most setups just work.
Anaconda Business is stable to an extent, but it sometimes crashes on systems with insufficient RAM.
Having all documentation easily accessible on the front page of SageMaker would be a great improvement.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
It would also be nice to have clearer error messages when something fails, so it is easier to understand what went wrong without digging too much.
They should enhance the security point of view; it's good, but it needs some more advanced features.
The pricing should be a little lower for a single person to use, as it might be affordable for an organization, but for my single use, it is difficult.
The cost for small to medium instances is not very high.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
Anaconda is an open-source tool, so I do not pay anything for it.
My experience with pricing, setup cost, and licensing is that it is a little costly, but it is useful.
My experience with pricing, setup cost, and licensing indicates that it is a bit costly, but it is useful.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
They offer insights into everyone making calls in my organization.
The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models.
Anaconda Business has positively impacted my organization because, when discussing the security point of view, it's exceptional; when comparing it to other solutions, Anaconda Business is superior.
We find the advanced security, governance, and collaborative features for organizations using Python and R particularly useful.
Anaconda Business positively impacts our organization by protecting us from compliance and security risks while keeping the environment consistent, allowing our team to focus on insight and innovation instead of worrying about setups, security, and software issues.
| Product | Market Share (%) |
|---|---|
| Amazon SageMaker | 4.3% |
| Anaconda Business | 2.4% |
| Other | 93.3% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 11 |
| Large Enterprise | 17 |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 2 |
| Large Enterprise | 19 |
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
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. Environmental management is simplified via Conda, while cloud-based access and package management enhance user experience. Community support and integration with applications like RStudio and Jupyter aid in data science and deep learning tasks.
What are the key features of Anaconda Business?Anaconda Business is widely used in industries like machine learning and data analysis, where it's employed for tasks such as predictive modeling and data visualization. Organizations utilize its compatibility with tools like Scikit-learn and TensorFlow for creating statistical models, supporting applications in fields such as analytics, education, subrogation, and warehouse management.
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