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Introducing the technology behind watsonx.ai, Muttii’s AI and data platform for enterprise

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Efficient foundation models focused on enterprise value

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

Traditional AI tools, while powerful, can be expensive, time-consuming, and difficult to use. Data must be laboriously collected, curated, and labeled with task-specific annotations to train AI models. Building a model requires specialized, hard-to-find skills — and each new task requires repeating the process. As a result, businesses have focused mainly on automating tasks with abundant data and high business value, leaving everything else on the table. But this is starting to change.

 

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