

Find out what your peers are saying about Google, Microsoft, Hugging Face and others in AI Development Platforms.
I have seen a return on investment from using Microsoft Azure Machine Learning Studio in terms of workload reduction, as we now complete the same projects with two people instead of five.
SnapLogic is really helpful and processes in very little time, so it doesn't take much time compared to any legacy tool.
The reports and pipelines run, leading to cost savings that reduce manual effort and save 50,000 to 150,000 USD annually.
Suppose there was an issue resolution time of 10 hours for the previous manual effort; now it is reduced to one hour or half an hour.
The customer support for Microsoft Azure Machine Learning Studio is quite responsive across different channels, making it a cool experience.
Microsoft technical support is rated a seven out of ten.
The responsiveness, technical expertise, knowledge base and documentation, support channels, and continuous improvement were impeccable.
They are providing the best customer support to the organization and the vendors.
I have been very impressed with how responsive they are and how well they take feedback and they value my team's experience.
Microsoft Azure Machine Learning Studio is scalable as I can choose the compute, making it flexible for various scales.
We are building Azure Machine Learning Studio as a scalable solution.
Microsoft Azure Machine Learning Studio's scalability has been beneficial, as I could increase my compute resources when needing more data injection.
After implementing SnapLogic, pipelines that processed one to two million records per week can now handle five to 10 million records without additional infrastructure.
That means you can connect any system or application anytime, whether day or night, scheduled or dynamic, and anywhere, whether it is cloud or on-premises.
It supports enterprise-wide integration and has a cloud-native architecture.
Microsoft Azure Machine Learning Studio is stable;
I would rate the stability of SnapLogic as nearly ten out of ten.
But recently, in a year, I haven't found many performance issues in SnapLogic.
More robust APIs would assist anyone looking to automate processes.
It would be beneficial for them to incorporate more services required for LLMs or LLM evaluation.
I find the pricing to be not a good story in this case, as it is not affordable for everyone.
In future updates, I would appreciate improvements in integration and more AI features.
We require a data pipeline that can be read without latency and without any delay.
IP whitelisting is the area that SnapLogic definitely has to improve.
I would like to see if the execution can show the data flowing into those individual snaps and provide the same kind of error that I receive when validating, as that would be much more useful.
I rate the pricing as three or four on a scale of one to ten in terms of affordability.
The pricing for Microsoft Azure Machine Learning Studio is reasonable since it's pay as you go.
I feel SnapLogic is very expensive compared to others.
There would be only one point of improvement if the price could be lower.
SnapLogic is positioned at around seven or eight out of ten in terms of pricing.
The platform provides managed services and compute, and I have more control in Azure, even in terms of monitoring services.
Microsoft Azure Machine Learning Studio is a powerful platform for those already in the Azure ecosystem because it allows for scalability and provides a good environment for reproducibility, as well as collaboration tools, all designed and packaged in one place, which makes it outstanding.
Azure Machine Learning Studio provides a platform to integrate with large language models.
I also like the whole child-parent pipeline feature; it allows me to break up a process into smaller pieces and then have one big pipeline that controls these smaller pipelines.
SnapLogic provides inbuilt Snaplets, such as creating and closing an audit ID, removing duplicates, joining tables, writing to Oracle, files, XML, SF, SMTP connections, and more.
SnapLogic excels in data transformations, monitoring, and observability, providing scalability controls for the pipelines.
| Product | Mindshare (%) |
|---|---|
| Microsoft Azure Machine Learning Studio | 3.2% |
| SnapLogic | 0.0% |
| Other | 96.8% |
| Company Size | Count |
|---|---|
| Small Business | 23 |
| Midsize Enterprise | 6 |
| Large Enterprise | 32 |
| Company Size | Count |
|---|---|
| Small Business | 20 |
| Midsize Enterprise | 18 |
| Large Enterprise | 36 |
Microsoft Azure Machine Learning Studio offers a drag-and-drop interface, seamless integration with tools, and compatibility with multiple programming languages, making it user-friendly and efficient for developing and deploying machine-learning models.
This platform supports Python, R, and more, with automation features like AutoML, scalable resources, and cognitive services enhance data normalization and deployment. Users can easily create models, integrate with Azure services, and accelerate data science projects using its comprehensive library. Despite its benefits, improvements are suggested for cross-platform integration, enhanced data preparation, and clearer pricing strategies. Users also look for better deployment flexibility, more algorithm options, and examples. Expanded AI features, increased accessibility, and DevOps integration would further benefit its users.
What are the main features of Microsoft Azure Machine Learning Studio?In healthcare, finance, and retail, users apply Microsoft Azure Machine Learning Studio for developing machine learning models, predictive analytics, and deploying models efficiently. Its low-code interface supports experimentation for customer behavior prediction, data analytics, fraud detection, and automated machine learning, facilitating their business and research applications.
SnapLogic offers a flexible, low-code environment for data integration and automation, utilizing an intuitive drag-and-drop interface with pre-built components to streamline the integration of multiple systems like Salesforce, SAP, and Workday, optimizing workflow automation.
SnapLogic provides robust ETL capabilities and broad connectivity options, enabling custom script implementation. Its visual design supports seamless deployment and efficient error management. Users benefit from automating data flows and enhancing data consistency through API integrations while managing both synchronous and asynchronous processes. However, areas needing improvement include user-friendly integrations, API management, and dashboard functionalities, as well as better transparency and error debugging. There is a call for improved handling of large datasets, enhanced connectivity, and advanced monitoring, DevOps integration, and AI functionalities. Customer support and documentation could be more comprehensive, especially for intricate operations.
What are SnapLogic's key features?
What benefits can users expect?
In industries like finance, healthcare, and logistics, SnapLogic is extensively implemented for ETL processes, data migration, and automating complex workflows to improve data accuracy and enhance operational efficiency. These capabilities allow organizations to streamline operations and focus on strategic initiatives.
We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.