787: MLOps: The Job and The Key Tools, with Demetrios Brinkmann
May 28, 2024
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Demetrios Brinkmann, an MLOps expert, shares insights on MLOps, LLMOps, and DevOps. He discusses developing an online MLOps community, using third-party APIs, and building online communities. The episode also covers tools like LlamaIndex and Ollama for scaling LLMs, as well as guidance on data pipeline engineering using Kubernetes and YAML files.
MLOps streamlines machine learning model deployment, while data engineering fundamentals are vital for success in MLOps.
Building a successful online community in tech and AI requires maximizing member engagement and minimizing creator focus.
Deep dives
MLOps Explained
MLOps, a subset of DevOps, ensures machine learning models can be put into production efficiently and repeatedly. Tools like llama index, oLama, lang chain, and dspi streamline LLM operations.
The Role of Data Engineers
Data engineers play a crucial role in the machine learning lifecycle, focusing on data pipelines and preparation. Understanding data engineering fundamentals sets a strong foundation in MLOps.
Community Building Insights
Building a community requires significant effort and involvement to foster many-to-many interactions. Minimizing creator focus and maximizing member engagement enhances a community's success.
Guiding Principles for MLOps
Starting with managed cloud services like SageMaker or Azure ML can ease entry into MLOps. Learning data pipelines and exploring the entire data and ML lifecycle sets a solid groundwork for MLOps success.
MLOps, how to build an online community, and tools for scaling LLMs: In this episode, Demetrios Brinkmann speaks to Jon Krohn about the similarities and differences between LLMOps, MLOps and DevOps, and why this should matter to companies looking to hire such engineers. You will also hear how to get involved in the MLOps community wherever you are in the world, and how you can start developing great products with the available tools.