NVIDIA

JANUARY 6, 2025

With Cosmos added to the three-computer solution, developers gain a data flywheel that can turn thousands of human-driven miles into billions of virtually driven miles amplifying training data quality.

Robotics 125

Robotics Software Development Data Quality AI Modeling 125

IBM Journey to AI blog

SEPTEMBER 3, 2024

“This is across all industries and disciplines, from transforming HR processes and marketing transformations through branded content to contact centers or software development.” These include so-called small language models and non-generative models, such as forecasting models , which require a narrower data set.

Generative AI 308

Generative AI LLM Large Language Models AI 308

AWS Machine Learning Blog

DECEMBER 4, 2024

This framework creates a central hub for feature management and governance with enterprise feature store capabilities, making it straightforward to observe the data lineage for each feature pipeline, monitor data quality , and reuse features across multiple models and teams.

ML 88

ML Machine Learning Generative AI AI 88

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 Data Science 98

AWS Machine Learning Blog

DECEMBER 11, 2024

In our case, where we have several applications built in-house, as well as third-party software backed by Amazon S3, we make heavy use of Amazon Q connector for Amazon S3, and as well as custom connectors weve written. Software Development Manager based in Seattle with over a decade of experience at AWS. Jonathan Garcia is a Sr.

Generative AI 93

Generative AI Data Ingestion Chatbots Software Engineer 93

AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

LLM 112

LLM Large Language Models Natural Language Processing Machine Learning 112

NVIDIA

NOVEMBER 12, 2024

This integration reduced development and future management costs by approximately 50% while improving the expressiveness of the avatars, according to AiHUB. The unsung stars here are software development kits — those bundles of tools, libraries and documentation that cut the guesswork out of innovation.

Data Quality 107

Data Quality AI AI Large Language Models 107

Unite.AI

MAY 6, 2024

The bulk of Persistent Systems business comes from building software for enterprises, how has the advent of generative AI transformed how your team operates? The advent of generative AI (GenAI) has transformed how our team operates at Persistent, particularly in enterprise software development.

Automation 147

Automation Software Engineer Generative AI Software Development 147

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. It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices.

ML 90

ML Machine Learning Auto-complete Auto-classification 90

AWS Machine Learning Blog

FEBRUARY 18, 2025

Recognizing this challenge as an opportunity for innovation, F1 partnered with Amazon Web Services (AWS) to develop an AI-driven solution using Amazon Bedrock to streamline issue resolution. Creating ETL pipelines to transform log data Preparing your data to provide quality results is the first step in an AI project.

Generative AI 71

Generative AI ETL LLM AI 71

AWS Machine Learning Blog

OCTOBER 15, 2024

Next, the SageMaker Ground Truth Plus team sets up data labeling workflows, which changes the batch status to In progress. Annotators label the data, and you complete your data quality check by accepting or rejecting the labeled data. Rejected objects go back to annotators to re-label.

ML 79

ML Machine Learning Software Development Artificial Intelligence 79

AWS Machine Learning Blog

JULY 8, 2024

The Data Quality Check part of the pipeline creates baseline statistics for the monitoring task in the inference pipeline. Within this pipeline, SageMaker on-demand Data Quality Monitor steps are incorporated to detect any drift when compared to the input data.

Data Scientist 111

Data Scientist ML Engineer Machine Learning Data Science 111

DataRobot Blog

JULY 21, 2022

Data aggregation such as from hourly to daily or from daily to weekly time steps may also be required. Perform data quality checks and develop procedures for handling issues. Typical data quality checks and corrections include: Missing data or incomplete records Inconsistent data formatting (e.g.,

Machine Learning 98

Machine Learning Data Drift Categorization Data Scientist 98

AWS Machine Learning Blog

JUNE 23, 2023

In this section, we demonstrate how to perform feature engineering on the data from Snowflake using SageMaker Data Wrangler’s built-in capabilities. You can use the report to help you clean and process your data. For Analysis type , choose Data Quality and Insights Report. Choose Create.

Auto-complete 86

Auto-complete Auto-classification ML Data Quality 86

AWS Machine Learning Blog

NOVEMBER 7, 2024

The AWS managed offering ( SageMaker Ground Truth Plus ) designs and customizes an end-to-end workflow and provides a skilled AWS managed team that is trained on specific tasks and meets your data quality, security, and compliance requirements. Hasan helps design, deploy and scale Generative AI and Machine learning applications on AWS.

Generative AI 107

Generative AI Machine Learning AI AI 107

AWS Machine Learning Blog

APRIL 24, 2023

After confirming that the data quality is acceptable, we go back to the data flow and use Data Wrangler’s Data Quality and Insights Report. Refer to Get Insights On Data and Data Quality for more information. Choose the plus sign next to Data types , then choose Add analysis.

Data Quality 77

Data Quality Deep Learning Machine Learning ML 77

IBM Journey to AI blog

AUGUST 12, 2024

RAG implementations involve combining LLMs with external data sources to enhance their knowledge and decision-making capabilities. This integration increases the complexity of AI systems, requiring robust governance frameworks to manage data quality, model performance, and compliance.

Automation 159

Automation Generative AI AI AI 159

Unite.AI

JUNE 24, 2024

It’s been fascinating to see the shifting role of the data scientist and the software engineer in these last twenty years since machine learning became widespread. Having worn both hats, I am very aware of the importance of the software development lifecycle (especially automation and testing) as applied to machine learning projects.

Data Scientist 130

Data Scientist Large Language Models Machine Learning Software Engineer 130

O'Reilly Media

NOVEMBER 28, 2023

Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3% AI users are definitely facing these problems: 7% report that data quality has hindered further adoption, and 4% cite the difficulty of training a model on their data.

Generative AI 135

Generative AI AI AI Data Analysis 135

Unite.AI

AUGUST 15, 2024

This is the common belief that if you just build cool software, people will line up to buy it. This never works, and the solution is a robust marketing process connected with your software development process. What role do large language models (LLMs) play in Tamr’s data quality and enrichment processes?

Machine Learning 147

Machine Learning Computer Scientist LLM Large Language Models 147

ODSC - Open Data Science

JULY 26, 2024

In particular, you’ll focus on tabular (or structured) synthetic data and the privacy-preserving benefits of working with synthetic data. You’ll even get hands-on with the open-source tool (DataLLM) and create tabular synthetic data yourselves. Gen AI in Software Development. What should you be looking for?

Data Scientist 52

Data Scientist Robotics Metadata Data Science 52

AWS Machine Learning Blog

NOVEMBER 15, 2023

Therefore, when the Principal team started tackling this project, they knew that ensuring the highest standard of data security such as regulatory compliance, data privacy, and data quality would be a non-negotiable, key requirement. He has 20 years of enterprise software development experience.

Data Ingestion 120

Data Ingestion Metadata NLP Data Scientist 120

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. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements.

ML Engineer 88

ML Engineer Data Scientist Convolutional Neural Networks ML 88

AWS Machine Learning Blog

FEBRUARY 21, 2023

With this option, you are testing the new model and minimizing the risks of a low-performing model, and you can compare both models’ performance with the same data. SageMaker deployment guardrails Guardrails are an essential part of software development. She is also the Co-Director of Women In Big Data (WiBD), Denver chapter.

ML 81

ML Software Development Automation Metadata 81

ODSC - Open Data Science

OCTOBER 15, 2023

Outerbounds’ platform is valuable for businesses that want to improve their data quality and identify potential problems early on. Active learning is a type of machine learning that involves iteratively querying a human for labels for data points that are most informative for the model.

Machine Learning 52

Machine Learning Data Science Artificial Intelligence Artificial Intelligence 52

AWS Machine Learning Blog

JULY 31, 2023

He has more than 25 years of experience with technology, including cloud solution development, machine learning, software development, and data center infrastructure. Indrajit is an AWS Enterprise Sr. Solutions Architect. In his role, he helps customers achieve their business outcomes through cloud adoption.

Auto-classification 91

Auto-classification Machine Learning ML Auto-complete 91

AWS Machine Learning Blog

APRIL 17, 2023

Model Model Partitioning and Optimization Engine Quantization Batch Size Tensor Parallel Degree Number of Workers Inference Latency P50 (ms) Inference Latency P90 (ms) Inference Latency P99 (ms) Data Quality flan-t5-xxl FasterTransformer FP32 4 4 1 327.39 Rohith Nallamaddi is a Software Development Engineer at AWS.

Prompt Engineering 93

Prompt Engineering Prompt Engineer Deep Learning Machine Learning 93

AWS Machine Learning Blog

NOVEMBER 16, 2023

Data Management – Efficient data management is crucial for AI/ML platforms. Regulations in the healthcare industry call for especially rigorous data governance. It should include features like data versioning, data lineage, data governance, and data quality assurance to ensure accurate and reliable results.

Data Scientist 109

Data Scientist ML Data Science Automation 109

Snorkel AI

FEBRUARY 27, 2023

This is a platform that supports this new data-centric development loop. This is then used to train models, and those models then power feedback and analyses that guide how to improve the quality of your data and therefore of your models.

Machine Learning 52

Machine Learning Large Language Models AI AI 52

Snorkel AI

FEBRUARY 27, 2023

This is a platform that supports this new data-centric development loop. This is then used to train models, and those models then power feedback and analyses that guide how to improve the quality of your data and therefore of your models.

Machine Learning 52

Machine Learning Large Language Models AI AI 52

Dlabs.ai

MARCH 30, 2022

We started with data loading and preprocessing, fixing optimization issues to allow the model to process years of historical across all 8,000 stores. Several breakthroughs enabled us to fix data quality issues within the dataset. The approach ultimate delivered a solution that satisfied all the stakeholders. team loves to do.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Explainability 52

Bugra Akyildiz

MAY 25, 2024

Automated Query Optimization: By understanding the underlying data schemas and query patterns, ChatGPT could automatically optimize queries for better performance, indexing recommendations, or distributed execution across multiple data sources. gradients of energies to compute forces).

ChatGPT 59

ChatGPT Machine Learning Data Analysis OpenAI 59

The MLOps Blog

APRIL 17, 2023

Then you must specify, analyze, test, and manage them throughout the software development lifecycle. Creating user stories , analyzing them, and validating requirements are all parts of requirements development that deserve their own article. The front end is one of the clients for this layer. What talent is available to build?

Metadata 52

Metadata Data Scientist Explainability ML 52

Chatbots Life

SEPTEMBER 11, 2023

It provides a detailed overview of each library’s unique contributions and explains how they can be combined to create a functional system that can detect and correct linguistic errors in text data. Training data quality and bias: ML-based grammar checkers heavily rely on training data to learn patterns and make predictions.

NLP 59

NLP Natural Language Processing Python Algorithm 59

The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. Git is a distributed version control system for software development.

ETL 52

ETL Data Drift Machine Learning ML 52

Mlearning.ai

MAY 16, 2023

Once the data is loaded into the data warehouse, it can be queried by business analysts and data scientists to perform various analyses such as customer segmentation, product recommendations, and trend analysis.

Explainability 52

Explainability ETL Big Data Machine Learning 52

Pickl AI

JULY 18, 2023

This “write once, run anywhere” capability allows developers to create applications that are not tied to a specific operating system, increasing portability and flexibility. It supports the handling of large and complex data sets from different sources, including databases, spreadsheets, and external files.

Data Science 52

Data Science Data Scientist Python Business Intelligence 52