AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. Ingest the prepared data into the feature group by using the Boto3 SDK.

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AWS Machine Learning Blog

OCTOBER 2, 2024

This is often referred to as platform engineering and can be neatly summarized by the mantra “You (the developer) build and test, and we (the platform engineering team) do all the rest!” Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

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ODSC - Open Data Science

MARCH 27, 2023

The Next Generation of Low-Code Machine Learning Devvret Rishi | Co-founder and Chief Product Officer | Predibase In this session, you’ll explore declarative machine learning, a configuration-based modeling interface, which provides more flexibility and simplicity when implementing cutting-edge machine learning.

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AWS Machine Learning Blog

JUNE 27, 2023

When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Ensuring data quality, governance, and security may slow down or stall ML projects.

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The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. Pay-as-you-go pricing makes it easy to scale when needed.

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The MLOps Blog

AUGUST 11, 2023

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Many questions regarding building machine learning pipelines and systems have already been answered and come from industry best practices and patterns. How should the machine learning pipeline operate?

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ODSC - Open Data Science

JANUARY 10, 2024

Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting This talk will outline the complexity of feature engineering from raw entity-level data, the reduction in complexity that comes with composable compute graphs, and an example of the working solution. Sign me up!

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DataRobot Blog

SEPTEMBER 13, 2022

The future of business depends on artificial intelligence and machine learning. According to IDC , 83% of CEOs want their organizations to be more data-driven. Data scientists could be your key to unlocking the potential of the Information Revolution—but what do data scientists do? What Do Data Scientists Do?

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AWS Machine Learning Blog

OCTOBER 11, 2024

Data lineage and auditing – Metadata can provide information about the provenance and lineage of documents, such as the source system, data ingestion pipeline, or other transformations applied to the data. This information can be valuable for data governance, auditing, and compliance purposes.

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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The MLOps Blog

MARCH 21, 2023

Moving across the typical machine learning lifecycle can be a nightmare. From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. How to understand your users (data scientists, ML engineers, etc.).

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

For production deployment, the no-code recipes enable easy assembly of the data ingestion pipeline to create a knowledge base and deployment of RAG or agentic chains. These solutions include two primary components: a data ingestion pipeline for building a knowledge base and a system for knowledge retrieval and summarization.

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