mlopsnow.com
MLOps Now supports deploying, serving, monitoring, or operating AI and machine-learning systems.
NASGym
NASGym is a proof-of-concept OpenAI Gym environment for Neural Architecture Search. It implements reinforcement learning steps for NAS and encodes networks using the Neural Structure Code from BlockQNN. A network is modeled as a list of NSCs, an action adds a layer, and the reward is accuracy after early-stop training. CIFAR-10 is the default dataset. The code and documentation may contain minor bugs.
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Uses
- CIFAR-10 dataset (default) Source: github.com
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MLOps 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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