Precisely

AUGUST 3, 2023

For example, workflow automation (43%), AI/ ML (41%), and DataOps (31%) are complementary technologies that enable organizations to automate data management and data processes so they can improve data quality even when people and skills are scarce.

DataOps 98

DataOps Data Quality Analytics Analytics 98

AWS Machine Learning Blog

APRIL 17, 2023

You can integrate a Data Wrangler data preparation flow into your machine learning (ML) workflows to simplify data preprocessing and feature engineering, taking data preparation to production faster without the need to author PySpark code, install Apache Spark, or spin up clusters. Sovik Kumar Nath is an AI/ML solution architect with AWS.

AWS 100

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AWS Machine Learning Blog

JULY 31, 2023

Although machine learning (ML) can provide valuable insights, ML experts were needed to build customer churn prediction models until the introduction of Amazon SageMaker Canvas. Additional key topics Advanced metrics are not the only important tools available to you for evaluating and improving ML model performance.

ML 96

ML ML Data Preparation Machine Learning 96

Alation

JANUARY 25, 2022

This “analysis” is made possible in large part through machine learning (ML); the patterns and connections ML detects are then served to the data catalog (and other tools), which these tools leverage to make people- and machine-facing recommendations about data management and data integrations.

DataOps 52

DataOps SQL ML ML 52

DataRobot Blog

SEPTEMBER 13, 2022

They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value. If you’re just getting started with AI and ML, technology can help you bridge gaps in your workforce and institutional knowledge. BARC ANALYST REPORT. Download Now.

Data Scientist 52

Data Scientist ML ML AI 52

ODSC - Open Data Science

JULY 20, 2023

Popular Machine Learning Libraries, Ethical Interactions Between Humans and AI, and 10 AI Startups in APAC to Follow Demystifying Machine Learning: Popular ML Libraries and Tools In this comprehensive guide, we will demystify machine learning, breaking it down into digestible concepts for beginners, including some popular ML libraries to use.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

IBM Journey to AI blog

SEPTEMBER 5, 2024

Emerging technologies and trends, such as machine learning (ML), artificial intelligence (AI), automation and generative AI (gen AI), all rely on good data quality. DevOps and DataOps are practices that emphasize developing a collaborative culture.

Data Governance 75

Data Governance Data Quality DataOps Artificial Intelligence 75

Alation

APRIL 4, 2023

Troubleshooting data issues , for an exploding number of disjointed systems and tools, breaks self-service for data users and creates gaps in visibility for dataOps. Building data pipelines is challenging, and complex requirements (as well as the separation of many sources) leads to a lack of trust.

DataOps 52

DataOps Data Engineering Data Engineering Data Engineering 52

Alation

SEPTEMBER 14, 2021

Stewardship Workbench: This feature uses AI and ML to automate the discovery of candidate data stewards based on who is actually using data. Given this, data governance should be a key enabler of DataOps. It improves governance processes and makes DataOps processes less constraining upon organizations.

Data Governance 52

Data Governance DataOps Machine Learning Machine Learning 52

Precisely

SEPTEMBER 18, 2023

Data observability is a key element of data operations (DataOps). It enables a big-picture understanding of the health of your organization’s data through continuous AI/ML-enabled monitoring – detecting anomalies throughout the data pipeline and preventing data downtime.

Data Observability 52

Data Observability Data Quality Data Pipeline DataOps 52

DataRobot Blog

JUNE 27, 2022

Monitoring Modern Machine Learning (ML) Methods In Production. Given the numerous variables that may change, how does the financial institution develop a robust monitoring strategy, and apply them in the context of ML models? Driving Innovation with AI: Getting Ahead with DataOps and MLOps. Monitoring Model Metrics.

ML 59

ML ML Machine Learning Machine Learning 59

Precisely

DECEMBER 12, 2023

Data observability is a foundational element of data operations (DataOps). Analysis : Processing the information about your enterprise data, assessing historical trends, and detecting outliers – all with the use of AI/ML for automated intelligent analysis.

Data Observability 52

Data Observability Data Quality Data Pipeline Machine Learning 52

BAIR

FEBRUARY 17, 2024

Optimization Often in ML, maximizing the quality of a compound system requires co-optimizing the components to work well together. Operation: LLMOps and DataOps. For example, if you want to answer RAG questions in 100 milliseconds, should you budget to spend 20 ms on the retriever and 80 on the LLM, or the other way around?

AI 145

AI AI DataOps Data Pipeline 145

BAIR

FEBRUARY 18, 2024

Optimization Often in ML, maximizing the quality of a compound system requires co-optimizing the components to work well together. Operation: LLMOps and DataOps. For example, if you want to answer RAG questions in 100 milliseconds, should you budget to spend 20 ms on the retriever and 80 on the LLM, or the other way around?

AI 40

AI AI DataOps Data Pipeline 40

Precisely

FEBRUARY 10, 2023

Advanced analytics and AI/ML continue to be hot data trends in 2023. Read our Report Improving Data Integrity and Trust through Transparency and Enrichment Data trends for 2023 point to the need for enterprises to govern and manage data at scale, using automation and AI/ML technology.

DataOps 52

DataOps Data Observability ML ML 52

Precisely

FEBRUARY 9, 2024

Trusting your data is the cornerstone of successful AI and ML (machine learning) initiatives, and data integrity is the key that unlocks the fullest potential. Without data integrity, you risk compromising your AI and ML initiatives due to unreliable inferences and biases that don’t fuel business value.

Data Quality 69

Data Quality DataOps Data Silos Artificial Intelligence 69

Snorkel AI

OCTOBER 31, 2023

Our researchers recently demonstrated the potency of programmatic dataops when they saved hundreds of hours preparing data to intruction-tune the RedPajama LLM. Smaller ML models enhance data privacy by using less data, reducing overfitting, simplifying audits, and lowering resource requirements. Our researchers did it in two days.

AI 111

AI AI DataOps Data Governance 111

Snorkel AI

OCTOBER 31, 2023

Our researchers recently demonstrated the potency of programmatic dataops when they saved hundreds of hours preparing data to intruction-tune the RedPajama LLM. Smaller ML models enhance data privacy by using less data, reducing overfitting, simplifying audits, and lowering resource requirements. Our researchers did it in two days.

AI 103

AI AI DataOps Data Governance 103

Snorkel AI

OCTOBER 31, 2023

Our researchers recently demonstrated the potency of programmatic dataops when they saved hundreds of hours preparing data to intruction-tune the RedPajama LLM. Smaller ML models enhance data privacy by using less data, reducing overfitting, simplifying audits, and lowering resource requirements. Our researchers did it in two days.

AI 52

AI AI DataOps Data Governance 52