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Docker CTO Explains How Docker Can Support AI Efforts

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Contenuto fornito da The New Stack Podcast and The New Stack. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da The New Stack Podcast and The New Stack o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

Docker CTO Justin Cormack reveals that Docker has been a go-to tool for data scientists in AI and machine learning for years, primarily in specialized areas like image processing and prediction models. However, the release of OpenAI's ChatGPT last year sparked a significant surge in Docker's popularity within the AI community.

The focus shifted to large language models (LLMs), with a growing interest in the retrieval-augmented generation (RAG) stack. Docker's collaboration with Ollama enables developers to run Llama 2 and Code Llama locally, simplifying the process of starting and experimenting with AI applications. Additionally, partnerships with Neo4j and LangChain allow for enhanced support in storing and retrieving data for LLMs.

Cormack emphasizes the simplicity of getting started locally, addressing challenges related to GPU shortages in the cloud. Docker's efforts also include building an AI solution using its data, aiming to assist users in Dockerizing applications through an interactive notebook in Visual Studio Code. This tool leverages LLMs to analyze applications, suggest improvements, and generate Docker files tailored to specific languages and applications.

Docker's integration with AI technologies demonstrates a commitment to making AI and Docker more accessible and user-friendly.

Learn more from The New Stack about AI and Docker:

Artificial Intelligence News, Analysis, and Resources

Will GenAI Take Jobs? No, Says Docker CEO

Debugging Containers in Kubernetes — It’s Complicated

  continue reading

856 episodi

Artwork
iconCondividi
 
Manage episode 386295526 series 75006
Contenuto fornito da The New Stack Podcast and The New Stack. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da The New Stack Podcast and The New Stack o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.

Docker CTO Justin Cormack reveals that Docker has been a go-to tool for data scientists in AI and machine learning for years, primarily in specialized areas like image processing and prediction models. However, the release of OpenAI's ChatGPT last year sparked a significant surge in Docker's popularity within the AI community.

The focus shifted to large language models (LLMs), with a growing interest in the retrieval-augmented generation (RAG) stack. Docker's collaboration with Ollama enables developers to run Llama 2 and Code Llama locally, simplifying the process of starting and experimenting with AI applications. Additionally, partnerships with Neo4j and LangChain allow for enhanced support in storing and retrieving data for LLMs.

Cormack emphasizes the simplicity of getting started locally, addressing challenges related to GPU shortages in the cloud. Docker's efforts also include building an AI solution using its data, aiming to assist users in Dockerizing applications through an interactive notebook in Visual Studio Code. This tool leverages LLMs to analyze applications, suggest improvements, and generate Docker files tailored to specific languages and applications.

Docker's integration with AI technologies demonstrates a commitment to making AI and Docker more accessible and user-friendly.

Learn more from The New Stack about AI and Docker:

Artificial Intelligence News, Analysis, and Resources

Will GenAI Take Jobs? No, Says Docker CEO

Debugging Containers in Kubernetes — It’s Complicated

  continue reading

856 episodi

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