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.
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.
AI/ML projects can run up big bills on compute. With Spark Operator, you can take advantage of spot instances and dynamic executor allocation, which can deliver big savings. Here's how to very simply set it up in MLRun.