We use the product for deploying machine learning models. We use it for the machine learning model development process.

We use the solution to extract financial information and contractual data from unstructured documents.

I use it for modeling large amounts of production data. We don't have the time and it's a large amount of production data. So, it's not physically possible to eliminate or find the co-relations, run it through, basically setting and coding in Python. So it's much easier. 

You just have your drag and drop. So if you have the Python knowledge for that, it's very good. We basically suggest that these people to use it as well. 

I use Amazon SageMaker to develop modules with data stored in AWS, extracted from SAP. After building the modules, I deploy them to assess their performance and efficiency.

We use Amazon SageMaker for model deployment, hosting, and monitoring.

I use SageMaker to use a "bring-your-own-model" setup. For SageMaker AutoML, we're fine and happy with it. It is restricted because you can't move through multiple algorithms. It seems to work only with two. One of the things I am doing is prototyping, and it's proving quite difficult to get our model working how we want it to. It's proving complex with many moving parts, and the documentation is only partially helpful. SageMaker requires a lot of work to get it working.

I spent the last four months trying to get a prototype working and exploring to bring in a model while exploring alternate models and making prototypes work. We've stepped back to AutoML for now. We might be using EKS, so we bring our containers. Within the containers, we can work with what we need to work with.

I use the solution since it is good. I have no issues with the solution as it suits my needs. Amazon SageMaker was used in our company to train an ML model. One of the trainers in our organization used Amazon SageMaker to train an ML model. I haven't had the opportunity to use products other than Amazon SageMaker. I am satisfied with Amazon SageMaker.

My company uses Amazon SageMaker since we are into data analytics involved in predictions and focusing on various model executions, working with some top companies. Most of the use cases of the solution for my company stem from the fact that we need to understand various customer chain models, including customer retention or customer acquisition models, to leverage more revenue. Sometimes, the solution functions in batch mode or real-time mode. In case a customer contacts an IVR agent or the customer support team for help, we do modeling in real-time and deliver to Amazon SageMaker endpoint, ensuring how the robotics part responds to the queries of the customer.

I use the solution to build machine learning models and deploy them on endpoints.

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.

I mainly use SageMaker for deploying, using, and running our models.

We are a solution provider that is concentrating on migrating our customers from on-premises to the cloud, and Amazon SageMaker is one of the products that we implement for our customers.

SageMaker is an AI platform, and I have been working on creating a solution that uses SageMaker and DeepLens to recognize people for access control. It will automatically log people who are coming and leaving. The second use case that we are working on is a system that recognizes cars by reading license plates and then opening a gate automatically to let them into the parking area.

AI, in general, has not yet been heavily used in this region so I am working on three or four use cases.

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

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.

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.

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.

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.