MLOps Now supports deploying, serving, monitoring, or operating AI and machine-learning systems.
MLOps Guide
A guide explaining MLOps concepts and how to build an MLOps environment. It covers theory and a tutorial using tools such as DVC, CML, IBM Watson Machine Learning, IBM Watson OpenScale, Terraform and Cookiecutter. The guide is described as modular, allowing tools to be swapped according to project preferences and use cases.
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0MLOps Now supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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0Msty describes private AI for individuals, teams and organizations, with four products connected as one system: Studio, a private AI workspace; Go, for agents; Nexus, a model gateway hub; and Stack, for organizing knowledge. Studio pricing lists a free tier and an Aurum plan billed yearly at $149 per user.
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0Banana provides GPU inference hosting for AI teams. The platform scales GPUs up and down automatically and includes monitoring, business analytics, a CLI, SDKs, and an open automation API. Backends can be written with Potassium, an open-source HTTP framework. Paid Team and Enterprise tiers are offered.
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0SocialBu is a social media management tool for planning, scheduling, responding to, automating, and analyzing social media across connected accounts. It offers AI-assisted drafting and refinement of posts and captions, content curation, listening streams for mentions and keywords, a unified inbox, automation rules, and performance reports. A 7-day free trial is mentioned. An MCP Server connects compatible AI clients to supported tasks.
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0Groq is a platform for inference, described as one fully integrated platform for infrastructure, inference, and control. It offers tiers including GroqMetal for bare-metal infrastructure, GroqCore for an inference stack, and GroqAssured for governance, auditability, and control.
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0Hopsworks is a platform for building, deploying, and scaling production machine learning systems. It combines a feature store with MLOps tooling, using dual online and offline storage to manage, reuse, and serve features. The platform covers the ML lifecycle: train, deploy, and monitor. An O'Reilly book covers building batch, real-time, and LLM systems with feature stores.
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