ZenML Blog

The latest news, opinions and technical guides from ZenML.
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LLMOps
13 mins

LangGraph vs AutoGen: How are These LLM Workflow Orchestration Platforms Different?

In this LangGraph vs Autogen article, we explain the difference between these platforms and when to use which one for the best results.
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LLMOps
15 mins

LlamaIndex vs LangGraph: How are They Different?

In this LlamaIndex vs LangGraph article, we explain the differences between these platforms and when to use each one for optimal results.
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MLOps
13 mins

Here are the 7 Best Weights & Biases Alternatives for Better Experiment Tracking

Discover the top 7 Weights & Biases alternatives for better experiment tracking.
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MLOps
16 mins

Metaflow vs Kubeflow vs ZenML: Which ML Pipeline Tool Is Right for You?

In this Metaflow vs Kubeflow vs ZenML article, we explain the difference between these platforms and which one is the right ML pipeline tool for you.
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LLMOps
5 mins

ZenML's MCP Server Supports DXT: Making MLOps Conversations Frictionless

ZenML's new DXT-packaged MCP server transforms MLOps workflows by enabling natural language conversations with ML pipelines, experiments, and infrastructure, reducing setup time from 15 minutes to 30 seconds and eliminating the need to hunt across multiple dashboards for answers.
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MLOps
20 mins

9 Best Kedro Alternatives to Build Production-Ready Data Science Pipelines

Discover the best Kedro alternatives to build production-grade data science pipelines.
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MLOps
21 mins

We Reviewed 8 Best Prefect Alternatives for Machine Learning Teams

Discover the top 8 Prefect alternatives for machine learning teams.
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Newsletter Edition #16 - The future of LLMOps @ ZenML (Your Voice Needed)

We're expanding ZenML beyond its original MLOps focus into the LLMOps space, recognizing the same fragmentation patterns that once plagued traditional machine learning operations. We're developing three core capabilities: native LLM components that provide unified APIs and management across providers like OpenAI and Anthropic, along with standardized prompt versioning and evaluation tools; applying established MLOps principles to agent development to bring systematic versioning, evaluation, and observability to what's currently a "build it and pray" approach; and enhancing orchestration to support both LLM framework integration and direct LLM calls within workflows. Central to our philosophy is the principle of starting simple before going autonomous, emphasizing controlled workflows over fully autonomous agents for enterprise production environments, and we're actively seeking community input through a survey to guide our development priorities, recognizing that today's infrastructure decisions will determine which organizations can successfully scale AI deployment versus remaining stuck in pilot phases.
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LLMOps
14 mins

Here are the Top 7 LlamaIndex Alternatives to Build AI Production Agents

Discover the top 7 LlamaIndex alternatives to build AI production agents with ease.
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