AWS Machine Learning Blog

NOVEMBER 15, 2023

In order analyze the calls properly, Principal had a few requirements: Contact details: Understanding the customer journey requires understanding whether a speaker is an automated interactive voice response (IVR) system or a human agent and when a call transfer occurs between the two.

Data Ingestion 110

Data Ingestion Metadata NLP Data Scientist 110

IBM Journey to AI blog

SEPTEMBER 20, 2023

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps. IBM watsonx consists of the following: IBM watsonx.ai

Large Language Models 228

Large Language Models DevOps AI Modeling Automation 228

AWS Machine Learning Blog

DECEMBER 18, 2023

In this post, we will explore the potential of using MongoDB’s time series data and SageMaker Canvas as a comprehensive solution. MongoDB Atlas MongoDB Atlas is a fully managed developer data platform that simplifies the deployment and scaling of MongoDB databases in the cloud.

Data Extraction 103

Data Extraction Data Ingestion ML Machine Learning 103

The MLOps Blog

JANUARY 26, 2024

Keeping track of how exactly the incoming data (the feature pipeline’s input) has to be transformed and ensuring that each model receives the features precisely how it saw them during training is one of the hardest parts of architecting ML systems. One of the core principles of MLOps is automation. What is a feature store?

Machine Learning 73

Machine Learning Metadata ML Python 73

ODSC - Open Data Science

JULY 19, 2023

A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

The MLOps Blog

MARCH 12, 2024

Tools range from data platforms to vector databases, embedding providers, fine-tuning platforms, prompt engineering, evaluation tools, orchestration frameworks, observability platforms, and LLM API gateways. Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools.

Prompt Engineer 52

Prompt Engineer Prompt Engineering LLM Large Language Models 52

AWS Machine Learning Blog

MARCH 29, 2023

Quality prediction – The estimation of a quality variable on the basis of process variables for decision support or for automation. For example, how much kg/m 3 adhesive ingredient shall be ingested to achieve certain strength and elasticity. Two types of data sources exist for this use case.

ML 75

ML Machine Learning Algorithm Data Scientist 75

The MLOps Blog

MARCH 21, 2023

It should be able to version the project assets of your data scientists, such as the data, the model parameters, and the metadata that comes out of your workflow. Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69