AWS Machine Learning Blog

DECEMBER 15, 2023

In this post, we discuss how to bring data stored in Amazon DocumentDB into SageMaker Canvas and use that data to build ML models for predictive analytics. Without creating and maintaining data pipelines, you will be able to power ML models with your unstructured data stored in Amazon DocumentDB.

Machine Learning 106

Machine Learning Machine Learning AWS ML 106

The MLOps Blog

JUNE 27, 2023

It provides tools and components to facilitate end-to-end ML workflows, including data preprocessing, training, serving, and monitoring. Kubeflow integrates with popular ML frameworks, supports versioning and collaboration, and simplifies the deployment and management of ML pipelines on Kubernetes clusters.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

MAY 3, 2024

How Snowflake Helps Achieve Real-Time Analytics Snowflake is the ideal platform to achieve real-time analytics for several reasons, but two of the biggest are its ability to manage concurrency due to the multi-cluster architecture of Snowflake and its robust connections to 3rd party tools like Kafka.

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Mlearning.ai

FEBRUARY 6, 2023

When building your Processing Docker image, don't place any data required by your container in these directories. docker tag ${algorithm_name} ${fullname} docker push ${fullname} Running the SageMaker Processing job using the custom container Amazon SageMaker copies the data from S3 and then pulls the container.

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AWS Machine Learning Blog

APRIL 19, 2023

Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. By default, it downloads the appropriate native binary based on your OS, CPU architecture, and CUDA version, making it almost effortless to use.

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DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

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ODSC - Open Data Science

FEBRUARY 8, 2024

Answering these questions allows data scientists to develop useful data products that start out simple and can be improved and made more complex over time until the long-term vision is achieved. At the strategy level, we are not interested in what technologies we will use for data warehousing, data pipelines, serving models, etc.

AI 52

AI AI Data Science Data Scientist 52

phData

OCTOBER 28, 2024

However, if the tool supposes an option where we can write our custom programming code to implement features that cannot be achieved using the drag-and-drop components, it broadens the horizon of what we can do with our data pipelines. The procedure loads a file into the database from S3, a copy of the processed data in the Snowflake.

Python 52

Python ETL AWS Database 52

ODSC - Open Data Science

DECEMBER 9, 2024

SciKit-Learn : A popular machine learning library with consistent APIs for regression, classification, clustering, dimensionality reduction, and model selection techniques. Central hubs like GitHub and GitLab along with dedicated data science notebooks enable exposure to real-world projects, accelerating practitioner skills.

Data Science 40

Data Science Machine Learning Machine Learning Python 40