ZenML Blog

The latest news, opinions and technical guides from ZenML.
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LLMs
06 mins

Automating Lightning Studio ML Pipelines For Fine Tuning LLM (s)

In the AI world, fine-tuning Large Language Models (LLMs) for specific tasks is becoming a critical competitive advantage. Combining Lightning AI Studios with ZenML can streamline and automate the LLM fine-tuning process, enabling rapid iteration and deployment of task-specific models. This approach allows for the creation and serving of multiple fine-tuned variants of a model, with minimal computational resources. However, scaling the process requires resource management, data preparation, hyperparameter optimization, version control, deployment and serving, and cost management. This blog post explores the growing complexity of LLM fine-tuning at scale and introduces a solution that combines the flexibility of Lightning Studios with the automation capabilities of ZenML.
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New Features: Improved Sagemaker Orchestration, New DAG Visualizer, Skypilot with Kubernetes, and more

This release incorporates updates to the SageMaker Orchestrator, DAG Visualizer, and environment variable handling. It also includes Kubernetes support for Skypilot and an updated Deepchecks integration. Various other improvements and bug fixes have been implemented across different areas of the platform.
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7 mins

Using ZenML+ Databricks to Supercharge LLM Development

The integration of ZenML and Databricks streamlines LLM development and deployment processes, offering scalability, reproducibility, efficiency, collaboration, and monitoring capabilities. This approach enables data scientists and ML engineers to focus on innovation.
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5 mins

Boost Your MLOps Efficiency: Integrate ZenML and Comet for Better Experiment Tracking

This blog post discusses the integration of ZenML and Comet, an open-source machine learning pipeline management platform, to enhance the experimentation process. ZenML is an extensible framework for creating portable, production-ready pipelines, while Comet is a platform for tracking, comparing, explaining, and optimizing experiments and models. The combination offers seamless experiment tracking, enhanced visibility, simplified workflow, improved collaboration, and flexible configuration. The process involves installing ZenML and enabling Comet integration, registering the Comet experiment tracker in the ZenML stack, and customizing experiment settings.
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New Features: Python 3.12 Support, slimmer Client Package and More!

ZenML's latest release 0.66.0 adds support for Python 3.12, removes some dependencies for a slimmer Client package and adds the ability to view all your pipeline runs in the dashboard.
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MLOps
10 min

Orchestration Showdown: Dagster vs Prefect vs Airflow

Comparing Airflow, Dagster, and Prefect: Choosing the right orchestration tool for your data workflows.
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Tutorials
5 mins

Supercharge Open Source ML Workflows with ZenML And Skypilot

The combination of ZenML and SkyPilot offers a robust solution for managing ML workflows.
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Newsletter Edition #7 - Notebooks in Production: The eternal MLOps debate

A new ZenML newsletter featuring Istanbul cooking adventures, faster docker builds, and more
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MLOps
15 mins

Building Scalable Forecasting Solutions: A Comprehensive MLOps Workflow on Google Cloud Platform

MLOps on Google Cloud Platform streamlines machine learning workflows using Vertex AI and ZenML.
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