What is your primary use case for Amazon SageMaker?

Amazon SageMaker is my go-to platform for all my machine learning tasks, offering an end-to-end solution for every stage of the ML workflow. It begins with data collection, where SageMaker notebooks provide a versatile environment for programming with Python libraries, supporting a wide range of use cases. Within the same environment, I perform data cleaning and transformation efficiently, ensuring high-quality input for downstream tasks.

For data labeling, SageMaker Ground Truth is an invaluable tool, especially for multimodal datasets. I utilize the custom labeling option to accommodate the diverse nature of my data, ensuring precise annotations. SageMaker seamlessly integrates training, model development, and deployment into its ecosystem, allowing me to manage the entire ML pipeline without transitioning between platforms.


With its comprehensive suite of services, Amazon SageMaker serves as a robust, one-stop solution for my machine learning projects, simplifying and accelerating the journey from data preparation to production-ready models.

In my projects, I work with Amazon SageMaker to do face recognition using Amazon SageMaker and Amazon Lex. The platform is used for integrated development and utilizes features like Amazon Cognition for analyzing images and videos, as well as comparing images.

The primary use case for Amazon SageMaker involves developing on the cloud to utilize flexible compute resources. I use it when dealing with really large datasets that do not fit on my local laptop. I leverage SageMaker's more powerful GPU, CPU, and memory for these tasks.

We use Amazon SageMaker for a specific project involving an airline company to analyze historical flight data. Initially, we created a proof of concept locally and then developed a data pipeline to enhance the flow for large data models. This project benefited from SageMaker's ability to handle large data with TensorFlow, leading to a more efficient MLOps process.

Our primary use case for Amazon SageMaker involves saving infrastructure requirements by deploying machine learning or AI models. Instead of preparing new servers, which incurs a high cost, we use SageMaker to develop models temporarily. We use SageMaker services only for the short duration needed to create AI models and then terminate them.

We use Amazon SageMaker primarily for training and deploying end-to-end models for our specific use cases. We take models from the interface and deploy them to the staging environment, ensuring they are monitored 24/7. This tool is essential for deploying models.

In terms of the tool's use case, if it is serverless, and if the compute involved is not too high, or if it is a PoC kind of a thing, and you want the microservices kind of architecture to be going and go for a pay as you go model, you can use the tool. With the tool, you know what is happening, so maybe you can cut costs by going with an on-premises model and having a stable system for computing.

Amazon SageMaker is a collaborative tool for our data science projects. It allows us to integrate efficiently, write and review code, and access all the necessary project tools.

The primary use case for Amazon SageMaker is leveraging its compute power, particularly for tasks like securing LMM notebooks using node instances. Additionally, its GPU capabilities are valuable for executing large language models. Users can create endpoints and access them from anywhere as needed.

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

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 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.

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.

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.

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

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

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.

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

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 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.

This is a solution that we have provided to one of our clients. It is being used for its inbuilt data science models. We are building regression models for forecasting demand. The client is in the business of consumer goods and they would like to be able to perform campaign-level forecasts. It is deployed on their AWS Cloud and all of the data is on Amazon Redshift.

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.

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.