O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Without clarity in metrics, it’s impossible to do meaningful experimentation. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

JANUARY 19, 2024

The exam tests general knowledge of the platform and applies to multiple roles, including administrator, developer, data analyst, data engineer, data scientist, and system architect. Candidates for the exam are tested on ML, AI solutions, NLP, computer vision, and predictive analytics.

Data Science 126

Data Science Machine Learning Predictive Modeling Experimentation 126

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. AGI wouldn’t just perceive its surroundings; it would understand them.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

O'Reilly on Data

MARCH 31, 2020

This has serious implications for software testing, versioning, deployment, and other core development processes. No company wants to dry up and go away; and at least if you follow the media buzz, machine learning gives companies real competitive advantages in prediction, planning, sales, and almost every aspect of their business. (To

Management 332

Management Machine Learning Experimentation Metrics 332

Cloudera

FEBRUARY 16, 2022

They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. Figure 04: Applied Machine Learning Prototypes (AMPs). By logging the performance of every combination of search parameters within an experiment, we can choose the optimal set of parameters when building a model.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

O'Reilly on Data

OCTOBER 19, 2021

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. Not only is data larger, but models—deep learning models in particular—are much larger than before. Model Operations.

IT 346

IT Testing Experimentation Software 346

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Testing and Data Observability. Production Monitoring and Development Testing.

Testing 307

Testing Machine Learning Consulting Data Quality 307