𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐨𝐧 𝐀𝐖𝐒

Building generative AI applications requires 𝑏𝑎𝑙𝑎𝑛𝑐𝑖𝑛𝑔 ℎ𝑖𝑔ℎ-𝑞𝑢𝑎𝑙𝑖𝑡𝑦, 𝑐𝑜ℎ𝑒𝑟𝑒𝑛𝑡 𝑜𝑢𝑡𝑝𝑢𝑡𝑠 𝑤𝑖𝑡ℎ 𝑑𝑎𝑡𝑎 𝑝𝑟𝑖𝑣𝑎𝑐𝑦 𝑎𝑛𝑑 𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑦.

Managing data privacy and mitigating security risks are crucial when handling proprietary data with foundation models (FMs).

𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬:
• 𝐸𝑛𝑠𝑢𝑟𝑖𝑛𝑔 ℎ𝑖𝑔ℎ-𝑞𝑢𝑎𝑙𝑖𝑡𝑦, 𝑎𝑐𝑐𝑢𝑟𝑎𝑡𝑒 𝑜𝑢𝑡𝑝𝑢𝑡𝑠
• 𝑀𝑎𝑛𝑎𝑔𝑖𝑛𝑔 𝑑𝑎𝑡𝑎 𝑝𝑟𝑖𝑣𝑎𝑐𝑦 𝑎𝑛𝑑 𝑠𝑒𝑐𝑢𝑟𝑖𝑡𝑦
• 𝐻𝑖𝑔ℎ 𝑐𝑜𝑚𝑝𝑢𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑐𝑜𝑠𝑡𝑠 𝑎𝑛𝑑 𝑟𝑒𝑠𝑜𝑢𝑟𝑐𝑒𝑠 𝑓𝑜𝑟 𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔 𝑚𝑜𝑑𝑒𝑙𝑠

𝐊𝐞𝐲 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: 𝐀𝐦𝐚𝐳𝐨𝐧 𝐁𝐞𝐝𝐫𝐨𝐜𝐤
Amazon Bedrock offers a fully managed service providing access to high-performing FMs from AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API.

It allows 𝐪𝐮𝐢𝐜𝐤, 𝐩𝐫𝐢𝐯𝐚𝐭𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐅𝐌𝐬 with your data and seamless integration and deployment using AWS tools, eliminating the need to manage infrastructure.

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬:
• 𝑃𝑟𝑜𝑚𝑝𝑡 𝐸𝑛𝑔𝑖𝑛𝑒𝑒𝑟𝑖𝑛𝑔:
• 𝑍𝑒𝑟𝑜-𝑠ℎ𝑜𝑡 𝑝𝑟𝑜𝑚𝑝𝑡𝑖𝑛𝑔
• 𝐹𝑒𝑤-𝑠ℎ𝑜𝑡 𝑝𝑟𝑜𝑚𝑝𝑡𝑖𝑛𝑔
• 𝐶ℎ𝑎𝑖𝑛-𝑜𝑓-𝑡ℎ𝑜𝑢𝑔ℎ𝑡 𝑝𝑟𝑜𝑚𝑝𝑡𝑖𝑛𝑔
• 𝑅𝑒𝐴𝑐𝑡
• 𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑡𝑖𝑜𝑛
• 𝑅𝑒𝑡𝑟𝑖𝑒𝑣𝑎𝑙 𝐴𝑢𝑔𝑚𝑒𝑛𝑡𝑒𝑑 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛
• 𝐴𝑔𝑒𝑛𝑡𝑠

𝐌𝐨𝐝𝐞𝐥 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧:
• 𝐹𝑖𝑛𝑒-𝑡𝑢𝑛𝑖𝑛𝑔
• 𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑒𝑑 𝑝𝑟𝑒-𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔
• 𝑅𝑒𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔 𝑜𝑟 𝑡𝑟𝑎𝑖𝑛𝑖𝑛𝑔 𝑓𝑟𝑜𝑚 𝑠𝑐𝑟𝑎𝑡𝑐ℎ

𝑊𝑎𝑛𝑡 𝑡𝑜 𝑙𝑒𝑎𝑟𝑛 𝑚𝑜𝑟𝑒 𝑑𝑒𝑡𝑎𝑖𝑙𝑠 𝑎𝑏𝑜𝑢𝑡 𝑒𝑎𝑐ℎ 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑣𝑒 𝐴𝐼 𝑎𝑝𝑝𝑟𝑜𝑎𝑐ℎ, 𝑐𝑜𝑚𝑚𝑒𝑛𝑡 𝑏𝑒𝑙𝑜𝑤