SageMaker Serverless Inference illustrates Amazon’s philosophy for ML workloads
Amazon just unveiled Serverless Inference, a new option for SageMaker, its fully managed machine learning (ML) service. The goal for Amazon SageMaker Serverless Inference is to serve use cases with intermittent or infrequent traffic patterns, lowering total cost of ownership (TCO) and making the service easier to use.
Read More →Massaging AI language models for fun, profit and ethics
Do AI language models really demonstrate intelligence? What about morality? Is it ok to tweak them, and if yes, who gets to do this, and how do the rest of us know?
Read More →Running AI workloads is coming to a virtual machine near you, powered by GPUs and Kubernetes
Run:AI offers a virtualization layer for AI, aiming to facilitate AI infrastructure. It's seeing lots of traction and just raised a $75M Series C funding round. Here's how the evolution of the AI landscape has shaped its growth.
Read More →What will applied AI look like in 2022?
AI adoption has skyrocketed throughout the last 18 months. Besides Joe McKendrick, who wrote the foundational piece on HBR, professionals who work on AI would readily attest to this statement. Google search seems to be in on this not-so-secret too: When prompted with “AI adoption,” its auto-complete spurts out “skyrocketed over the last 18 months”.
Read More →OctoML announces the latest release of its platform, exemplifies growth in MLOps
OctoML is announcing the latest release of its platform to automate the deployment of production-ready models across the broadest array of clouds, hardware devices and machine learning acceleration engines.
Read More →The state of AI in 2021: Machine learning in production, MLOps and data-centric AI
With lessons learned from operationalizing AI, the emphasis is shifting from shiny new models to perhaps more mundane, but practical aspects such as data quality and data pipeline management
Read More →The state of MLOps in 2021
MLOps is the art and science of bringing machine learning to production, and it means many things to many people. The State of MLOps is an effort to define and monitor this market.
Read More →More than words: Shedding light on the data terminology mess
Data management, data governance, data observability, data fabric, data mesh, DataOps, MLOps, AIOps. It's a data terminology mess out there. Let's try and untangle it, because there's more to words than lingo.
Read More →OctoML scores $28M to go to market with open source Apache TVM, a de facto standard for MLOps
The open source Apache TVM project is becoming a de facto standard in MLOps, and OctoML is gearing its commercialization and scale up
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