IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). Large language models (LLMs) have taken the field of AI by storm. IBM watsonx consists of the following: IBM watsonx.ai

Large Language Models 238

Large Language Models DevOps Data Science AI Modeling 238

AWS Machine Learning Blog

NOVEMBER 15, 2023

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics.

Data Ingestion 120

Data Ingestion Metadata NLP Data Scientist 120

Bugra Akyildiz

FEBRUARY 24, 2024

Streaming data platforms: Apache Kafka and Apache Flink enable real-time ingestion and processing of user actions, clickstream data, and product catalogs, feeding fresh data to the models. This translates to longer session durations, increased page views, and deeper user engagement.

Large Language Models 52

Large Language Models LLM Data Ingestion Neural Network 52

ODSC - Open Data Science

JULY 19, 2023

Data Estate: This element represents the organizational data estate, potential data sources, and targets for a data science project. Data Engineers would be the primary owners of this element of the MLOps v2 lifecycle. The Azure data platforms in this diagram are neither exhaustive nor prescriptive.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

The MLOps Blog

MARCH 21, 2023

They work with other users to make sure the data reflects the business problem, the experimentation process is good enough for the business, and the results reflect what would be valuable to the business. What do they want to accomplish? Let’s look at the healthcare vertical for context.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69