Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps.

Machine Learning 98

Machine Learning Software Development Data Drift DevOps 98

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 126

ML Machine Learning Data Science Data Drift 126

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 96

Data Scientist Data Quality Python ML 96

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. All models built within DataRobot MLOps support ethical AI through configurable bias monitoring and are fully explainable and transparent. Governance and Trust.

Data Drift 52

Data Drift DevOps Machine Learning Data Scientist 52

Snorkel AI

JULY 24, 2023

Suddenly, non-technical users witnessed the LLM-backed chatbot’s ability to regurgitate knowledge, explain jokes and write poems. When models are pretrained, data is the main means for customization and fine-tuning of the models,” Gartner® said. Data is the best way to program models. Data quality matters.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Large Language Models DevOps 52

Snorkel AI

JULY 24, 2023

Suddenly, non-technical users witnessed the LLM-backed chatbot’s ability to regurgitate knowledge, explain jokes and write poems. When models are pretrained, data is the main means for customization and fine-tuning of the models,” Gartner® said. Data is the best way to program models. Data quality matters.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Large Language Models DevOps 52

Snorkel AI

JULY 24, 2023

Suddenly, non-technical users witnessed the LLM-backed chatbot’s ability to regurgitate knowledge, explain jokes and write poems. When models are pretrained, data is the main means for customization and fine-tuning of the models,” Gartner® said. Data is the best way to program models. Data quality matters.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Large Language Models DevOps 52

The MLOps Blog

APRIL 17, 2023

It should be possible to locate where the data and models for an experiment came from, so your data scientists can explore the events of the experiment and the processes that led to them. This unlocks two significant benefits: Reproducibility : Ensuring every experiment your data scientists run is reproducible.

Metadata 52

Metadata Data Scientist Explainability ML 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. Is it a black-box model, or can the decisions be explained?

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? Model serving.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Pickl AI

MAY 15, 2024

Data Quality and Standardization The adage “garbage in, garbage out” holds true. Inconsistent data formats, missing values, and data bias can significantly impact the success of large-scale Data Science projects. This builds trust in model results and enables debugging or bias mitigation strategies.

Data Scientist 52

Data Scientist Data Science Machine Learning Data Quality 52

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. Can you explain the concept of ‘data democracy' in the context of today's AI-driven business environment?

DevOps 147

DevOps Data Quality Machine Learning Generative AI 147