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.

This episode is brought to you by AWS Inferentia (https://go.aws/3zWS0au) and AWS Trainium (https://go.aws/3ycV6K0). Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:
• What MLOps is [03:51]
• About LLMOps [12:06]
• About LlamaIndex and Ollama [18:29]
• Insights from Demetrios’ MLOps survey [20:49]
• Guidance for using third-party APIs [40:18]
• Recommendations for building an online community in tech and AI [47:07]

Additional materials: www.superdatascience.com/787

The Super Data Science Podcast: Machine Learning | AI | Success

787: MLOps: The Job and The Key Tools, with Demetrios Brinkmann

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast.

As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact.

Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy.

We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

New tracks tagged #MLOps Community