AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. Applying these techniques allows ML practitioners to reduce the amount of data required to train an ML model.

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Pickl AI

JUNE 6, 2024

Data scrubbing is often used interchangeably but there’s a subtle difference. Cleaning is broader, improving data quality. This is a more intensive technique within data cleaning, focusing on identifying and correcting errors. Data scrubbing is a powerful tool within this cleaning service.

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Heartbeat

OCTOBER 30, 2023

Once the best model is identified, it is usually deployed in production to make accurate predictions on real-world data (similar to the one on which the model was trained initially). Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. But this is only sometimes the case.

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TensorFlow

MARCH 10, 2023

It can also include constraints on the data, such as: Minimum and maximum values for numerical columns Allowed values for categorical columns. Before a model is productionized, the Contract is agreed upon by the stakeholders working on the pipeline, such as the ML Engineers, Data Scientists and Data Owners.

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AWS Machine Learning Blog

OCTOBER 28, 2024

Early and proactive detection of deviations in model quality enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues without having to monitor models manually or build additional tooling. Ajay Raghunathan is a Machine Learning Engineer at AWS.

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AWS Machine Learning Blog

APRIL 26, 2023

You may have gaps in skills and technologies, including operationalizing ML solutions, implementing ML services, and managing ML projects for rapid iterations. Ensuring data quality, governance, and security may slow down or stall ML projects. This may often be the same team as cloud engineering.

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The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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Snorkel AI

JUNE 9, 2023

Full session recap The Opportunity of Data-Centric AI in Insurance Alejandro Zarate Santovena, lecturer at Columbia University and Managing Director at Marsh , asserted that AI and foundation models have a lot of potential to disrupt the insurance industry.

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Snorkel AI

JUNE 9, 2023

Full session recap The Opportunity of Data-Centric AI in Insurance Alejandro Zarate Santovena, lecturer at Columbia University and Managing Director at Marsh , asserted that AI and foundation models have a lot of potential to disrupt the insurance industry.

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The MLOps Blog

MARCH 15, 2023

Automation : Automating as many tasks to reduce human error and increase efficiency. Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. This includes data quality, privacy, and compliance.

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Viso.ai

JANUARY 30, 2024

They use automation tools like the caret package in R and Pipelines in scikit-learn. Data leakage occurs when training data is not truly representative of the population at large – source. This is a bigger deal with raw or unstructured data that engineers and developers might be using to feed the machine learning program.

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Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream.

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Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream.

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Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations. He is focused on AI/ML technology, ML model management, and ML governance to improve overall organizational efficiency and productivity.

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Snorkel AI

OCTOBER 12, 2023

Leveraging Data-Centric AI for Document Intelligence and PDF Extraction Extracting entities from semi-structured documents is often a challenging task, requiring complex and time-consuming manual processes. Wayfair does this by automating image tagging using a data-centric approach.

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Snorkel AI

OCTOBER 12, 2023

Leveraging Data-Centric AI for Document Intelligence and PDF Extraction Extracting entities from semi-structured documents is often a challenging task, requiring complex and time-consuming manual processes. Wayfair does this by automating image tagging using a data-centric approach.

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Snorkel AI

OCTOBER 12, 2023

Leveraging Data-Centric AI for Document Intelligence and PDF Extraction Extracting entities from semi-structured documents is often a challenging task, requiring complex and time-consuming manual processes. Wayfair does this by automating image tagging using a data-centric approach.

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The MLOps Blog

JULY 18, 2023

It’s an automated chief of staff that automates conversational tasks. We are aiming to automate that functionality so that every worker in an organization can have access to that help, just like a CEO or someone else in the company would. How do you ensure data quality when building NLP products?

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AWS Machine Learning Blog

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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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. Data preprocessing.

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The MLOps Blog

MARCH 21, 2023

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. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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The MLOps Blog

JANUARY 26, 2024

Responsibility for MLOps and the ML platform was split across three teams: 1 One team focused on making tools, setting up the environment for development and training for data scientists, and helping with the productionization work. This included maintaining the underlying infrastructure and working on model deployment automation.

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