Remove Data Processing Remove Deep Learning Remove Testing
article thumbnail

The DataOps Vendor Landscape, 2021

DataKitchen

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 300
article thumbnail

A Practitioner’s Guide to Deep Learning with Ludwig

Domino Data Lab

New tools are constantly being added to the deep learning ecosystem. For example, there have been multiple promising tools created recently that have Python APIs, are built on top of TensorFlow or PyTorch , and encapsulate deep learning best practices to allow data scientists to speed up research.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Cloudera AI Inference Service Enables Easy Integration and Deployment of GenAI Into Your Production Environments

Cloudera

The service is targeted at the production-serving end of the MLOPs/LLMOPs pipeline, as shown in the following diagram: It complements Cloudera AI Workbench (previously known as Cloudera Machine Learning Workspace), a deployment environment that is more focused on the exploration, development, and testing phases of the MLOPs workflow.

Metrics 73
article thumbnail

Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes.

article thumbnail

Deep learning for improved breast cancer monitoring using a portable ultrasound scanner

Insight

Segmentation Since a few patients had multiple images in the dataset, the data were separated, by patient, into three parts: training (80%), validation (10%), and testing (10%). The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. The box plot below shows a summary of the testing results.

article thumbnail

NLP Isn’t Enough. Leading Financial Services Companies Are Now Moving to Conversational AI.

CIO Business Intelligence

The very best conversational AI systems come close to passing the Turing test , that is, they are very difficult to distinguish from a human being. . In some parts of the world, companies are required to host conversational AI applications and store the related data on self-managed servers rather than subscribing to a cloud-based service.

article thumbnail

Will enterprises soon keep their best gen AI use cases under wraps?

CIO Business Intelligence

Helping software developers write and test code Similarly in tech, companies are currently open about some of their use cases, but protective of others. They now use what they learn about a program to help build unit tests. And unit tests are too tedious for humans to build reliably.