deepsense.ai

JULY 30, 2023

Other steps include: data ingestion, validation and preprocessing, model deployment and versioning of model artifacts, live monitoring of large language models in a production environment, monitoring the quality of deployed models and potentially retraining them. Of course, the desired level of automation is different for each project.

Large Language Models 76

Large Language Models LLM Machine Learning Automation 76

AWS Machine Learning Blog

NOVEMBER 22, 2023

Operational excellence in IDP means applying the principles of robust software development and maintaining a high-quality customer experience to the field of document processing, while consistently meeting or surpassing service level agreements (SLAs). This post focuses on the Operational Excellence pillar of the IDP solution.

IDP 84

IDP Data Extraction Automation ML 84

AWS Machine Learning Blog

NOVEMBER 22, 2023

Customers across all industries run IDP workloads on AWS to deliver business value by automating use cases such as KYC forms, tax documents, invoices, insurance claims, delivery reports, inventory reports, and more. Effectively manage your data and its lifecycle Data plays a key role throughout your IDP solution.

IDP 88

IDP Data Ingestion Automation Natural Language Processing 88

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. He has 20 years of enterprise software development experience.

Data Ingestion 111

Data Ingestion Metadata NLP Data Scientist 111

AWS Machine Learning Blog

MARCH 1, 2023

MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle. MLOps requires the integration of software development, operations, data engineering, and data science.

Data Ingestion 84

Data Ingestion DevOps Automation ML 84

AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. Choose the car-data-ingestion-pipeline.

ML 93

ML Data Ingestion Python Machine Learning 93

The MLOps Blog

MARCH 21, 2023

Version control for code is common in software development, and the problem is mostly solved. However, machine learning needs more because so many things can change, from the data to the code to the model parameters and other metadata. Automation is a good MLOps practice for speeding up all parts of that lifecycle.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69