Amazon SageMaker Reviews

I know about SageMaker and its capabilities, and what it can do, but I have not had any hands-on experience.

It's a machine learning platform for developers to create models.

There are pre-built solutions for everything. For example, if you want to build a deep learning model, we already have AlexNet, the internet, and all of the packages are inside. You don't have to recreate the same thing from scratch, but instead, you can use their models. You can use their model and use their data, then you can use your data.

I am a big fan of their computational storage capabilities. It's a relational database itself. It's a new SQL and you get different types of services. That is one of the best things that I like when doing my research.

I cannot quantify it as it is based on your requirements, but I can say that it's very flexible and you are able to increase all of the RAM and the GPU support.

They are doing a very good job on their end. They are evolving. I have learned that they have already integrated an IDE into Amazon SageMaker. They are doing a good job of evolving.

The pricing is complicated and should be simplified.

I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox. This would be beneficial for newcomers, especially those who are getting into the cloud space. They could explore this area and get all of the aspects including data engineering, data recognition, and data transformation.

I have been familiar with this solution for three months.

From my findings, it's quite stable.

Amazon promises that they will provide you with stability, and it is quite a stable platform.

If you are facing any issues it may be related to the computational storage capability that you opted for. For example, if you are opting for a full code row and you have a lot of data that is taking a lot of time, then you have to go back to retrieve it. That flexibility is within the AWS, but you have to bear the cost.

It's quite scalable.

The technical support is very good and I am satisfied with it.

I researched Amazon SageMaker on my own.

The initial setup is straightforward. It's not complex.

The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation. It is already decided, but if you want to have a look at how it is broken down or how they are calculating it, then they provide a tool where you can go and specify your options. These include what you want, how much storage, the RAM, and whether you want GPU support. You can include everything and then you can get the estimated cost.

AWS is an additional cost.

We are not with Anaconda Solutions, we use their packages. We are exploring their interface and it's capabilities. We are currently on a different tool, on a different platform. We are using their package managers to access the set of solutions deployed.

I am not exposed to Amazon SageMaker but I know it's capabilities. I know exactly what we can do and how we can do it. We have been provided with several solutions for image processing, speech processing, and text processing. They have provided a built-in solution for every task. You can use tools for deploying your model, you just have to plug and play.

There is no cessation from what I can see. Whatever they have in the industry, they can solve 98% of the use cases.

There is also data engineering which is quite important. It's where the real work is done.

Amazon has already provided a free slot for each of the services that we have done. With Amazon SageMaker, however, I have not seen that.

I have not yet explored everything, but they are doing good work.

In terms of the dashboard, I can say that I have not explored the visualization aspect very much, but they have their tools. I don't know how flexible it is and how much customization you can do. That's something on the visualization side that I don't enjoy very much. My interests are mostly towards data engineering or data science.

I would rate this solution a nine out of ten.

This is a solution that we have provided to one of our clients.

The client is in the business of consumer goods and they wanted to get accurate demand forecasts to evaluate performance of campaigns and optimize inventory.

The solution is an ensemble of regression models and is deployed on their AWS Cloud and all of the data is on Amazon Redshift. 

The deployment is easy and good. The documentation is pretty good also.

Integration with other AWS services is seamless.

The interface and the IDE could have some improvement. UX isn't bad but could be better.

Orchestration of the ML flow can be made easier (like ETL etc.)

Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier.

Adding certain AI functionalities similar to what DataRobot or Azure AI has would be really great.

I have been using Amazon SageMaker for four to five months.

This is a stable solution. We haven't seen any glitches as of yet.

It is scalable to a degree. We have used several open data sources and have found that for small data, it works well. However, as the volume of data increases, there are issues with respect to scalability.

In general, I would say that for small to medium volumes of data, this solution works well. For bigger data, there is room for improvement. 

We have a team of five people who are using SageMaker.

We did not need to contact technical support because the documentation is good and we have in-house expertise.

I have also used the Microsoft Azure Machine Learning Studio and Databricks, and the interface is a little better with these solutions. The Microsoft solution is really good in terms of user experience.

When it comes to deployment and integrating with cloud services, Amazon SageMaker is better as AWS.

I did not have trouble with the initial setup and I don't think that it was very complex. Overall, I would say that it is good.

We have a few experts here who helped with the implementation. The deployment took about a week to get everything ready.

Two people are suitable for maintenance and support.

My advice to anybody who is considering this solution is to think about using multiple cloud services. This solution is good but for complex business problems and big data, it gets a bit trickier. In terms of deployment, it is a clear winner.

From the cost point of view, it's relatively on the higher side.

Overall, there are a few improvements that I want but SageMaker is pretty good.

I would rate this solution a seven out of ten.

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.

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. 

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.  

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

The solution is quite stable. 

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

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

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. 

Initial setup is quite straightforward. 

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

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. 

We use this solution for Outlier Detection using Random Cut Forest. We intend to implement a Predictive modeling project starting in October and have not yet decided on the platform(s) we will utilize.

The challenge for us is balancing the Data Scientists, Technical vs. Analyst.

We are still learning the platform and will conduct more training as we evaluate it for other projects. The few projects we have done have been promising.

The most valuable features of this solution are the Random Cut Forest and the IDE.

I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time.

We use the solution as an OCR to extract text from documents, images, PDFs, etc. 

The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc. 

I am impressed with the tool's text extraction and its accuracy. 

The solution needs to be cheaper since it now charges per document for extraction. 

I have been using the solution for five years. 

I would rate the tool's stability a nine out of ten. 

I would rate the tool's scalability a nine out of ten and we use it once a week. 

The support's response to tickets is slow. 

Neutral

I would rate the product's deployment a nine out of ten since it's straightforward. The deployment gets completed within 20 minutes. You need one IT person and a developer to handle the deployment and maintenance. 

We have seen ROI with the tool's use. 

I would rate the solution's price a ten out of ten since it is very high. 

I recommend the tool for document processing and would rate it an eight out of ten. 

We are using Amazon SageMaker to forecast the models. We receive the data into Amazon S3 from the SAP HANA-based systems. Additionally, we are doing preprocessing and sampling for regular data.

The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code.

There are other better solutions for large data, such as Databricks.

I have been using Amazon SageMaker for three years.

The stability of Amazon SageMaker is good.

Amazon SageMaker is scalable to the project requirements.

The support from Amazon SageMaker has been positive. We create a ticket with our issues and they contact us with the solution.

I have previously used Databricks. 

The initial setup of Amazon SageMaker is straightforward. The solution is cloud-native making the process take a few minutes. Adding the extensions can take some time. We used CI/CD methods to implement the solution.

We have different teams and we had a team of two DevOps that did the implementation of the solution.

Databricks solution is less costly than Amazon SageMaker.

I rate Amazon SageMaker a seven out of ten.