11 posts in this category
How to build a production-ready financial report analysis pipeline using multiple specialized AI agents with ZenML for orchestration, SmolAgents for lightweight agent implementation, and LangFuse for observability and debugging.
FloraCast is a production-ready template that shows how to build a forecasting platform—config-driven experiments, model versioning/staging, batch inference, and scheduled retrains—with ZenML and Darts.
Learn how to build, fine-tune, and deploy multimodal LLMs using ZenML. Explore LLMOps best practices for deployment, real-time inference and model management.
The combination of ZenML and Neptune can streamline machine learning workflows and provide unprecedented visibility into experiments. ZenML is an extensible framework for creating production-ready pipelines, while Neptune is a metadata store for MLOps. When combined, these tools offer a robust solution for managing the entire ML lifecycle, from experimentation to production. The combination of these tools can significantly accelerate the development process, especially when working with complex tasks like language model fine-tuning. This integration offers the ability to focus more on innovating and less on managing the intricacies of your ML pipelines.
The combination of ZenML and SkyPilot offers a robust solution for managing ML workflows.
Cloud Composer (Airflow) vs Vertex AI (Kubeflow): How to choose the right orchestration service on GCP based on your requirements and internal resources.
An overview of MLOps principles, implementation strategies, best practices, and tools for managing machine learning lifecycles.
Taking large language models (LLMs) into production is no small task. It's a complex process, often misunderstood, and something we’d like to delve into today.
Seamlessly automating the journey from training to production, ZenML's new NLP project template offers a comprehensive MLOps solution for teams deploying Huggingface models to AWS Sagemaker endpoints. With its focus on reproducibility, scalability, and best practices, the template simplifies the integration of NLP models into workflows, complete with lineage tracking and various deployment options.
Deploying Huggingface models to AWS Sagemaker endpoints typically only requires a few lines of code. However, there's a growing demand to not just deploy, but to seamlessly automate the entire flow from training to production with comprehensive lineage tracking. ZenML adeptly fills this niche, providing an end-to-end MLOps solution for Huggingface users wishing to deploy to Sagemaker.
