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

Unite.AI

OCTOBER 16, 2023

While seemingly a variant of MLOps or DevOps, LLMOps has unique nuances catering to large language models' demands. Training Data : The essence of a language model lies in its training data. The data's quality and diversity significantly impact the model's accuracy and versatility.

Large Language Models 184

Large Language Models LLM Machine Learning Neural Network 184

The MLOps Blog

JUNE 27, 2023

Can you debug system information? 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. Can you compare images?

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

JULY 8, 2024

The need for MLOps at TWCo TWCo strives to help consumers and businesses make informed, more confident decisions based on weather. The Data Quality Check part of the pipeline creates baseline statistics for the monitoring task in the inference pipeline.

Data Scientist 107

Data Scientist ML Engineer Data Science Machine Learning 107

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 87

Data Drift Metadata Data Quality ML 87

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 82

Data Scientist Data Quality Python ML 82

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, NET, and AWS.

Data Science 52

Data Science DevOps Data Quality Machine Learning 52

AWS Machine Learning Blog

FEBRUARY 28, 2023

These challenges include a limited number of data science experts, the complexity of ML, and the low volume of data due to restricted Protected Health Information (PHI) and infrastructure capacity. No special permissions are required because the data doesn’t contain any sensitive information.

ML 81

ML Machine Learning Categorization NLP 81

Pickl AI

APRIL 27, 2023

Consumer Data Protection Will Be The Prime Concern As we move into the digital world, there will be a growing demand for systems that can assure data safety. Consumers are now more aware and want complete confidence when it comes to the information they share. It is one of the best e-learning platforms for Data Science.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Python 52

Viso.ai

SEPTEMBER 2, 2024

Verifying and validating annotations to maintain high data quality and reliability. Good understanding of spatial data, 2D and 3D geometry, and coordinate systems. Staying informed on advances in computer vision and image technology relevant to annotation tasks. cars in traffic, or a medical condition in healthcare.

Computer Vision 52

Computer Vision Software Engineer Convolutional Neural Networks Neural Network 52

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 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

AWS Machine Learning Blog

FEBRUARY 21, 2023

For general information on these patterns, refer to Take advantage of advanced deployment strategies using Amazon SageMaker deployment guardrails and Deployment guardrails. When the model update process is complete, SageMaker Model Monitor continually monitors the model performance for drifts into the model and data quality.

ML 80

ML Software Development Automation Metadata 80

The MLOps Blog

APRIL 17, 2023

They can’t be sure that a trained model (or models) will generalize to unseen data without monitoring and evaluating their experiments. The data science team can use this information to choose the best model, parameters, and performance metrics. Varying workflows so users can decide what they want to track.

Metadata 52

Metadata Data Scientist Explainability ML 52

The MLOps Blog

AUGUST 3, 2023

Depending on your size, you might have a data catalog. Maybe storing and emitting open lineage information, etc. 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. How to be a valuable MLOps Engineer?

ML 52

ML Data Scientist Software Engineer Machine Learning 52

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 Data Science 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

Unite.AI

NOVEMBER 21, 2024

Archana Joshi brings over 24 years of experience in the IT services industry, with expertise in AI (including generative AI), Agile and DevOps methodologies, and green software initiatives. GenAI processes vast amounts of data to provide actionable insights. They were facing scalability and accuracy issues with their manual approach.

DevOps 144

DevOps Automation Responsible AI Software Development 144