Gemma 2 is officially here! 🥳 Learn how you can access it → https://goo.gle/3RLQXUa Available in both 9B and 27B parameter sizes, Gemma 2 is higher performing than ever before. Download the weights on Kaggle and Hugging Face, or access the models in Google AI Studio. We’ve also released a new Gemma cookbook to help developers build their own applications and fine-tune Gemma 2 models for specific tasks responsibly.
Gemma 2 can benefit data engineers in a number of ways. 1. it can be used to improve the performance of data pipelines. 2. it can be used to automate tasks that are currently performed manually by data engineers. 3. it can be used to create new and innovative data engineering solutions. For example, Gemma 2 could be used to improve the performance of a data pipeline by identifying and eliminating bottlenecks. It could also be used to automate tasks such as data cleaning and transformation. Additionally, Gemma 2 could be used to create new and innovative data engineering solutions, such as real-time data processing systems and machine learning models. * Improve the performance of data pipelines by identifying and eliminating bottlenecks. * Automate tasks such as data cleaning and transformation. * Create new and innovative data engineering solutions, such as real-time data processing systems and machine learning models. I hope this helps!
💎💎💎 You can build with Gemma 2 in Haystack right away: https://www.linkedin.com/posts/silvanocerza_google-just-announced-and-released-gemma-activity-7212106803118055426-0V_b
🎉 Google for Developers can immediately try high- performance Gemma 2 models on the NVIDIA API catalog and download NVIDIA NIM microservices for local development. ➡ https://nvda.ws/3WLW35R
Can't wait!
Good luck!t
Very promising!
Growth Marketing Leader & Business Developer || Expert in Hacking Business Growth in AI, Web3, and FinTech Companies || Automation Expert
3moGemma 2 benefits developers by offering open-source, accessible AI models with 27 billion parameters. These models are designed for responsible AI development, allowing developers to experiment, innovate, and conduct robust model evaluations using tools like the LLM Comparator. This makes it easier to build and deploy AI applications effectively.