Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy
Ahsan Nabi Dar’s Post
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Absolutely, the advent of #AI marks a pivotal frontier in technology, #reshaping industries and #revolutionizing our approach to problem-solving. Being AI-ready is not just an advantage; it's a necessity for staying competitive in today's fast-paced digital landscape. Embracing AI means unlocking new efficiencies, enhancing decision-making, and driving innovation across all sectors. Organizations that invest in #AI #capabilities are poised to lead the charge into the future, leveraging data-driven insights to transform their operations and deliver unprecedented value. Let's gear up and get #AI-ready to harness the full potential of this transformative technology! 🚀 #AI #Innovation #FutureTech
Large Language Models (#LLMs) are evolving rapidly, and the demand for running them at scale is skyrocketing. #Ollama is stepping up to meet this need with its official container image on Docker Hub, excelling in local #deployments. However, there’s a catch for containerized environments: downloading #models requires the ollama serve command to be running within the #container. This extra step complicates automated deployments via #CI #CD #pipelines, making seamless model access challenging. To take your #model to production, it needs to be #container-ready—a feature Ollama has yet to fully support. The gap in #AIOps for smooth deployments gets blurred with the inference services available as an easy way to take it to production. Goal is to overcome obstacles and deliver solutions 😉 so here is to get you started on having your (O)#llama #production ready in a private #deployment from your #local to #cloud all in #oneshot . Enjoy Medium: https://lnkd.in/g9jqBQ8d dev.to: https://lnkd.in/gi_gGwam #AI #LLM #Ollama #llama3 #phi3 #mistral #S6overlay #AIOps #CI #CD #oneshot #container #docker #podmanEnjoy
Deploy Ollama with s6-overlay to serve and pull in one shot
dev.to
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The open source software platform MLflow which was first built by Databricks and JFrog Artifactory have announced a new machine learning (ML) lifecycle integration. With the help of the new integration, JFrog users now have a more efficient, end-to-end DevSecOps process for developing, managing, and delivering ML models and apps powered by generative artificial intelligence (GenAI) in addition to all other software development components. Businesses can verify the security and provenance of ML models and enable responsible AI practices by making each model traceable and immutable. https://lnkd.in/gZE4WSDa #AI #Databrick #JFrog #ml #MLFlow
Developers Can Now Enjoy A Secure AI Journey With JFrog, Which Seamlessly Integrates With Databricks' MLflow - AI Next
https://www.indianext.co.in
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MLflow is a comprehensive platform for managing the complete machine learning lifecycle. MinIO’s #AI SME Keith Pijanowski saves you time and effort by walking you through how to get MLflow and MinIO running using Docker, Inc Compose. https://hubs.li/Q01_Z0Hh0
Setting up a Development Machine with MLFlow and MinIO
blog.min.io
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MLflow is a comprehensive platform for managing the complete machine learning lifecycle. MinIO’s #AI SME Keith Pijanowski saves you time and effort by walking you through how to get MLflow and MinIO running using Docker, Inc Compose. https://hubs.li/Q01YFFX30
Setting up a Development Machine with MLFlow and MinIO
blog.min.io
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MLflow is a comprehensive platform for managing the complete machine learning lifecycle. MinIO’s #AI SME Keith Pijanowski saves you time and effort by walking you through how to get MLflow and MinIO running using Docker, Inc Compose. https://hubs.li/Q01YFN6f0
Setting up a Development Machine with MLFlow and MinIO
blog.min.io
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JFrog Empowers a Secure AI Journey for Developers, Integrates with Databricks’ MLflow for a Seamless Machine Learning Lifecycle https://lnkd.in/dXQB3ps2 #AITech365 #JFrog #JFrogArtifactory #JFrogSoftware #lifecycleintegration #LiquidSoftware #machinelearning #MLflow #news
JFrog Empowers a Secure AI Journey for Developers, Integrates with Databricks’ MLflow for a Seamless Machine Learning Lifecycle
https://aitech365.com
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“Aviary - Study stochastic parrots in the wild “. Funny name for this interesting project pointing to future direction of LLMOps: compare models before put them in production, rank models by quality and give latency and cost estimation before release them. Stochastic appeals to a probabilistic/randomness nature, and this is why AI engineering differs from SW Engineering due to its non-deterministic nature, where the pre-release task gain more importance because it requires to demonstrate the ability of the solution to have controlled the side-effects by using grounding techniques, prompt engineering, responsible AI validation, content moderation and other guardrails. #artificialintelligence #ai #llm #llmops #mlops #gpt
GitHub - ray-project/aviary: Ray Aviary - evaluate multiple LLMs easily
github.com
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MLflow is a comprehensive platform for managing the complete machine learning lifecycle. MinIO’s #AI SME Keith Pijanowski saves you time and effort by walking you through how to get MLflow and MinIO running using Docker, Inc Compose. https://hubs.li/Q01YFPlz0
Setting up a Development Machine with MLFlow and MinIO
blog.min.io
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In this post, we explored the implementation of gRPC for synchronous communication in microservices using Go. We learned about defining services using Protobufs, implementing clients and gateways, and testing the implementation. The use of gRPC provides advantages over JSON when optimization is important, and we've also covered the considerations for using generated models versus internal models. #AI #ML #Microservices #gRPC #Protobufs #SynchronousCommunication #GoProgramming #BigData #DataScience #DeepLearning
In this post, we explored the implementation of gRPC for synchronous communication in microservices using Go. We learned about defining services using Protobufs, implementing clients and gateways, and testing the implementation. The use of gRPC provides advantages over JSON when optimization is important, and we've also covered the considerations for using generated models versus internal mod...
dev.to
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Streamline the Development of Real-Time AI Applications with MindsDB Docker Extension With MindsDB, you can build AI-powered applications easily, even with no AI/ML experience. Follow along to learn how to set up MindsDB in Docker Desktop. Read mode on following blog post!
Streamline the Development of Real-Time AI Applications with MindsDB Docker Extension
https://www.docker.com
