To get started with Otto-m8, users need to have Docker installed and, for specific functionalities, the Ollama server running. The platform provides examples like OpenAI Langchain PDF Parsing and Huggingface Multimodal to demonstrate its capabilities. The roadmap for Otto-m8 includes features like basic chatbot and Huggingface workflows, function calling, custom code blocks, multimodality support for Huggingface and OpenAI, an SDK for workflow interaction, and enhanced observability and memory features for chatbots. The goal is to streamline workflow creation, editing, and redeployment, potentially incorporating ML model training via the UI.
Key takeaways:
- otto-m8 is a flowchart-based automation platform designed to run deep learning workloads with minimal to no code, allowing users to deploy AI models through an easy-to-use interface.
- The platform operates on an Input, Process, Output paradigm and deploys workflows as Docker containers, which can be used as APIs for integration with existing workflows or as standalone applications.
- otto-m8 supports various AI models, including traditional deep learning models and large language models, and provides examples such as OpenAI Langchain PDF Parsing and Huggingface Multimodal workflows.
- The roadmap for otto-m8 includes features like basic chatbot and Huggingface workflows, function calling, multimodality support, SDK for workflow interaction, and enhanced observability and memory for chatbots.