Data Quality, DevOps and Events - Artificial Intelligence Zone

McKinsey QuantumBlack on automating data quality remediation with AI

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

Data Quality Automation Data Scientist ML

McKinsey QuantumBlack on automating data quality remediation with AI

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

Data Quality Automation Data Scientist ML

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

FEBRUARY 27, 2024

In this case, we are developing a forecasting model, so there are two main steps to complete: Train the model to make predictions using historical data. Apply the trained model to make predictions of future events. Workflow A corresponds to preprocessing, data quality and feature attribution drift checks, inference, and postprocessing.

Machine Learning

Machine Learning DevOps Data Quality Data Scientist

Webinars

Generative AI Deep Dive: Advancing from Proof of Concept to Production

Understanding User Needs and Satisfying Them

Leading the Development of Profitable and Sustainable Products

How To Get Promoted In Product Management

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Remembering the 2023 Data Engineering Summit in Videos

ODSC - Open Data Science

FEBRUARY 21, 2024

Data-Planning to Implementation Balaji Raghunathan | VP of Digital Experience | ITC Infotech Over his 20+ year-long career, Balaji Raghunatthan has worked with cloud-based architectures, microservices, DevOps, Java, .NET, You can also get data science training on-demand wherever you are with our Ai+ Training platform.

DevOps

DevOps Data Science Data Quality Machine Learning

AWS’ Generative AI Strategy Starts to Take Shape and Looks a Lot Like Microsoft’s

TheSequence

DECEMBER 3, 2023

. 📝 Editorial: AWS’ Generative AI Strategy Starts to Take Shape and Looks a Lot Like Microsoft’s The AWS re:Invent conference has long been regarded as the premier event of the year for cloud computing. Bedrock now allows developers to integrate their own data sources to build RAG applications.

AI Strategy

AI Strategy Generative AI DevOps Data Quality

Application modernization overview

IBM Journey to AI blog

NOVEMBER 24, 2023

Application modernization is the process of updating legacy applications leveraging modern technologies, enhancing performance and making it adaptable to evolving business speeds by infusing cloud native principles like DevOps, Infrastructure-as-code (IAC) and so on.

Generative AI

Generative AI Auto-complete DevOps Automation

MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

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

Data Scientist Data Quality Python ML

MLOps Landscape in 2023: Top Tools and Platforms

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

Machine Learning Metadata Data Scientist Data Quality

Create SageMaker Pipelines for training, consuming and monitoring your batch use cases

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. This can be done in Studio or using Boto3.

Data Drift

Data Drift Metadata Data Quality ML

Top Synthetic Data Tools/Startups For Machine Learning Models in 2023

Marktechpost

JULY 17, 2023

Information created intentionally rather than as a result of actual events is known as synthetic data. Synthetic data is generated algorithmically and used to train machine learning models, validate mathematical models, and act as a stand-in for test production or operational data test datasets.

Machine Learning

Machine Learning Data Scientist Computer Vision Deep Learning

Definite Guide to Building a Machine Learning Platform

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

Machine Learning Data Scientist ML Metadata

Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

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

ML Machine Learning Categorization NLP

How to Build an Experiment Tracking Tool [Learnings From Engineers Behind Neptune]

The MLOps Blog

APRIL 17, 2023

Three reasons you need to build an experiment tracking tool Handling metadata and artifact lineage from data and model origins An experiment tracking tool can help your data scientists trace the lineage of experiment artifacts from their data and model origins, store the resulting metadata, and manage it.

Metadata

Metadata Data Scientist Explainability ML

Artificial Intelligence Zone

McKinsey QuantumBlack on automating data quality remediation with AI

McKinsey QuantumBlack on automating data quality remediation with AI

Webinars

Trending Sources

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

Webinars

Remembering the 2023 Data Engineering Summit in Videos

AWS’ Generative AI Strategy Starts to Take Shape and Looks a Lot Like Microsoft’s

Application modernization overview

MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

MLOps Landscape in 2023: Top Tools and Platforms

Create SageMaker Pipelines for training, consuming and monitoring your batch use cases

Top Synthetic Data Tools/Startups For Machine Learning Models in 2023

Definite Guide to Building a Machine Learning Platform

Extract non-PHI data from Amazon HealthLake, reduce complexity, and increase cost efficiency with Amazon Athena and Amazon SageMaker Canvas

How to Build an Experiment Tracking Tool [Learnings From Engineers Behind Neptune]

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