Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022. That is still in flux and being worked out.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JULY 24, 2023

“When models are pretrained, data is the main means for customization and fine-tuning of the models,” Gartner® said. Snorkel researchers recently demonstrated the power of data quality in collaboration with researchers at Together AI. Data is the best way to program models. Data quality matters.

Large Language Models 52

Large Language Models Artificial Intelligence Artificial Intelligence AI 52

Snorkel AI

JULY 24, 2023

“When models are pretrained, data is the main means for customization and fine-tuning of the models,” Gartner® said. Snorkel researchers recently demonstrated the power of data quality in collaboration with researchers at Together AI. Data is the best way to program models. Data quality matters.

Large Language Models 52

Large Language Models Artificial Intelligence Artificial Intelligence AI 52

AWS Machine Learning Blog

APRIL 21, 2023

See the following code: # Configure the Data Quality Baseline Job # Configure the transient compute environment check_job_config = CheckJobConfig( role=role_arn, instance_count=1, instance_type="ml.c5.xlarge", These are key files calculated from raw data used as a baseline.

Data Drift 74

Data Drift Metadata Data Quality ML 74

Pickl AI

APRIL 27, 2023

These key trends in data signs highlight the growing significance of this technology. Wrapping it up !!! In the years to come, automation will become a part of business operations.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Python 52

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

Machine Learning 134

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AWS Machine Learning Blog

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account). Refer to Operating model for best practices regarding a multi-account strategy for ML.

Data Scientist 73

Data Scientist Data Quality Python ML 73

DataRobot Blog

FEBRUARY 11, 2022

These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. DataRobot’s MLOps product offers a host of features designed to transform organizations’ user experience, firstly, through its model-monitoring agents. Governance and Trust.

Data Drift 52

Data Drift Machine Learning DevOps Data Scientist 52

AWS Machine Learning Blog

APRIL 26, 2023

Ensuring data quality, governance, and security may slow down or stall ML projects. Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. MLOps engineering – Focuses on automating the DevOps pipelines for operationalizing the ML use case.

ML 89

ML Machine Learning Data Science Data Drift 89

Marktechpost

JULY 17, 2023

The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

AWS Machine Learning Blog

FEBRUARY 28, 2023

If you want to add rules to monitor your data pipeline’s quality over time, you can add a step for AWS Glue Data Quality. And if you want to add more bespoke integrations, Step Functions lets you scale out to handle as much data or as little data as you need in parallel and only pay for what you use.

ML 71

ML Machine Learning Categorization NLP 71

The MLOps Blog

APRIL 17, 2023

Robustness You need an elastic data model to support: Varying team sizes and structures (a single data scientist only, or maybe a team of one data scientist, 4 machine learning engineers, 2 DevOps engineers, etc.). Varying workflows so users can decide what they want to track. Some will only track the post-training phase.

Metadata 52

Metadata Data Scientist Explainability ML 52

The MLOps Blog

AUGUST 3, 2023

One of the features that Hamilton has is that it has a really lightweight data quality runtime check. If you’re using tabular data, there’s Pandera. The data scientists are here with software engineers. ML platform team can be for this DevOps team. Related post MLOps Is an Extension of DevOps.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

AWS Machine Learning Blog

FEBRUARY 21, 2023

When the model update process is complete, SageMaker Model Monitor continually monitors the model performance for drifts into the model and data quality. She is currently focusing on combining her DevOps and ML background into the domain of MLOps to help customers deliver and manage ML workloads at scale.

ML 71

ML Automation Software Development Metadata 71

Unite.AI

MARCH 21, 2024

After that, I worked for startups for a few years and then spent a decade at Palo Alto Networks, eventually becoming a VP responsible for development, QA, DevOps, and data science. That led me to pursue engineering at Sharif University of Technology in Iran and later get my Ph.D.

DevOps 147

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The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. Model performance analysis and evaluation.

ML 98

ML Machine Learning Metadata Automation 98

The MLOps Blog

MARCH 21, 2023

” — Isaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 Monitoring Monitoring is an essential DevOps practice, and MLOps should be no different. Collaboration The principles you have learned in this guide are mostly born out of DevOps principles. My Story DevOps Engineers Who they are?

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69