ML is a key enabler for financial use cases, especially for risk-related requirements. Yet deploying ML models in enterprises is not always an efficient process: time to delivery is long and access to data is limited. Jiri Steuer from HCI shares his top tips and ideas for achieving MLOps efficiency.
Distributed ingestion is a great way to increase scalability for ML use cases with large datasets. But like any ML component, integrating and maintaining another tool introduces engineering complexity. Here's how to simplify it.
As we raise our glasses to the upcoming year, here are my predictions of what we'll see in the MLOps industry in 2023
The IDC MarketScape: Worldwide Machine Learning Operations Platforms 2022 Vendor Assessment is an annual study that evaluates vendors based on a comprehensive framework. It provides in-depth quantitative and qualitative assessments of MLOps solutions vendors in a long-form research report, to help buyers make important technology decisions that will create long term success.
Iguazio is thrilled to be named an Outperforming Leader in GigaOm’s latest report on MLOps. This recognition highlights our rigorous production-first approach to MLOps and differentiated capabilities that address the entire end to end lifecycle of AI/ML services.
Here's how to continuously deploy Hugging Face models into real business environments at scale, along with the required application logic with the help of MLRun.