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Allan Kirszberg
Coordenador Financeiro at Icatu
Real User
Top 20
Process data quickly, is easy to use and set up and comes with good technical support
Pros and Cons
  • "I like the solution's velocity, the speed with which it processes data, and its ease of use."
  • "A colleague of mind mentioned that the solution should have more options for the visualization of data."

What is our primary use case?

I believe we are using the new version. 

I use the solution for development and certain financial projections, such as making a business plan. I employ the solution for the amalgamation of big data. 
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I also use the solution for certain analytics deployment, such as that found in the commercial arena, for ascertaining the projections of sales. 

What is most valuable?

I like the solution's velocity, the speed with which it processes data, and its ease of use. It has good usability. 

What needs improvement?

A colleague of mind mentioned that the solution should have more options for the visualization of data. The visualization filtering could stand improvement. 

The documentation could be improved upon. 

The price could be better. Though we are satisfied with the solution, we feel the price to be a bit on the expensive side. We make use of another ETL solution, the PowerCenter. We would like to improve on and escalate Alteryx, but find it too expensive at the moment to do so. 

For how long have I used the solution?

I have been using Alteryx within the past 12 months. 

What do I think about the stability of the solution?

I believe the solution to be stable. 

What do I think about the scalability of the solution?

I believe the solution to be scalable, although I cannot say for certain, owing to the price. It We are not talking about a corporate solution at the moment. While we wish to improve upon and escalate the solution, I believe it is too expensive for us to do so at the present time. 

While I cannot speak about the future with certainty, we do have plans to increase the usage. We are satisfied with what we have at present. My area currently consists of four licenses and we would like to improve on this, to have more users. For the moment, we don't have definite plans to buy another license, certainly not in 2022, although, perhaps, in 2023. I sure hope so. 

How are customer service and support?

Technical support is good.

Which solution did I use previously and why did I switch?

We make use of another ETL solution, the PowerCenter.

We also make use of Databricks, which we use as a data science platform, although I cannot say how this compares with Alteryx. We use Databricks as a corporate ETL solution from the Data Lake. 

We use Microsoft Azure, as well.  

How was the initial setup?

The initial setup was easy.

The deployment was fast, I believe lasting one day. 

What about the implementation team?

We handled the setup on our own. 

I don't know how many people are needed for deployment and maintenance. 

What's my experience with pricing, setup cost, and licensing?

The price could be better. Though we are satisfied with the solution, we feel the price to be a bit on the expensive side. We make use of another ETL solution, the PowerCenter. We would like to improve on and escalate Alteryx, but find it too expensive at the moment to do so.

The licensing costs are around 26,000 reals, perhaps $6,500 a year.

The licensing is all inclusive. There are no further costs. 

What other advice do I have?

I don't make use of Alteryx myself, although my area, of which I am a manager, does. 

I am a customer of Alteryx. 

There are six people making use of the solution in our organization, two from compliance and one a data scientist. 

My colleage rates Alteryx as a nine out of ten. She says she loves it. 

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
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Full stack Data Analyst at a tech services company with 10,001+ employees
Real User
Plenty of features, powerful AutoML functionality, but better MLflow integration needed
Pros and Cons
  • "Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon."
  • "I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."

What is our primary use case?

I use a combination of Microsoft Azure Machine Learning Studio and Azure Databricks. I mostly use Azure Databricks for building a machine learning system. There are several workflows for a machine learning tuning system that involves data pre-processing, quick modeling pipelines that execute within a couple of seconds, and complex model pipelines, such as hyperparameters. Additionally, there is a setting to set different AutoML parameters. 

For the training and evaluation phase of the whole machine learning system, I use MLflow, for a testing system and a model serving system, which is one core component of Databricks. I use it for Model Register and it allows me to do many things, such as registering model info, logs, and evaluation metrics.

What is most valuable?

The newer version of this solution has better integration with automated ML processes and different APIs. I feel like it is quite powerful in terms of general machine learning features, such as training data handily by having different sampling methods and has more useful modeling parameter settings. People who are not data scientists or data analysts, can quickly use the platform and build models to leverage the data to do some predictive models.

Azure Machine Learning Studio's most valuable features are the package from Azure AutoML. It is quite powerful compared to the building of ML in Databricks or other AutoMLs from other companies, such as Google and Amazon. It has the most sophisticated set of categories of parameters. The data encodings and options are good and it has the most detailed settings for specifics models.

What needs improvement?

I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system.

The developers for this solution have not been as active in improving it as other solutions have had more improvements, such as Databricks.

Sometimes there might be some data drifting problems and this is what I am currently working on. For example, when our new data has a drift from the previous old data. I need to first work out a solution. Azure in Databricks or in Azure Machine Learning Studio both works fine. However, the normal data drifting solution is not working that well for the problem that I am facing. I am able to receive the distribution change and numerical metrics changes, but it will not inform me how to fix them.

For how long have I used the solution?

I have been using this solution for approximately three months.

Which solution did I use previously and why did I switch?

I use Databricks alongside this solution.

What other advice do I have?

I rate Microsoft Azure Machine Learning Studio a seven out of ten.

Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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Consultant at a tech services company with 501-1,000 employees
Consultant
Top 20
Great for automating pipelines and creation of API endpoints
Pros and Cons
  • "Allows you to create API endpoints."
  • "Lacking in some machine learning pipelines."

What is our primary use case?

Our primary use case for SageMaker is for developing end to end machine learning solutions and ready solutions for things such as computer vision or speech recognition or speech to text. It's basically providing off-the-shelf solutions. Our customers are generally medium to enterprise size companies. We're a partner of Amazon.

What is most valuable?

The most valuable feature of the solution is that it allows you to create API endpoints and that saves a lot of time for data scientists. 

What needs improvement?

The product has come a long way and they've added a lot of things, but in terms of improvement I would like to probably have features such as MLflow embedded into it.

Additional features I would like to see would include, as mentioned, MLflow and ML Pipelines which are more of a feature rich support of machine learning pipelines as well as scheduling machine learning pipelines, and visualization of machine learning pipelines.  

For how long have I used the solution?

I've been using this solution for about a year.

What do I think about the stability of the solution?

The solution is quite stable. 

What do I think about the scalability of the solution?

The solution is hosted on Amazon so it's quite scalable.

How are customer service and technical support?

The documentation is good so I haven't needed to use technical support. 

Which solution did I use previously and why did I switch?

SageMaker was the first cloud solution I've used but there are other products, such as Databricks or Google and Azure that have similar products. There are common features with all these products but I'd say that SageMaker has more features than Databricks. Azure has other features in addition to Databricks, but SageMaker has provided everything. 

How was the initial setup?

Initial setup is quite straightforward. 

What's my experience with pricing, setup cost, and licensing?

The pricing for the Notebook endpoints is a bit high, but generally reasonable. 

What other advice do I have?

I think for anyone using SageMaker it will help automate pipelines, and make it easier than doing the process manually. For anyone already on the AWS platform, they should definitely make use of it.

I would rate this product an eight out of 10. 

Disclosure: My company has a business relationship with this vendor other than being a customer: partner
Get our free report covering Microsoft, Amazon, Microsoft, and other competitors of Databricks. Updated: January 2022.
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