In a development, Amazon Bedrock introduces the ability to evaluate, compare and choose the optimal base models (FM) tailored to your specific needs. The model evaluation feature, now in preview, provides developers with a variety of evaluation tools, offering both automated and human benchmarking options.
The power of model evaluation
Model evaluations play a critical role at every stage of development. Developers can leverage the model evaluation feature to build generative artificial intelligence (ai) applications with unprecedented ease. This includes experimenting with different models in the platform’s gaming environment, streamlining the iterative process by incorporating automated evaluations, and ensuring quality through human reviews during the release phase.
Simplified automatic model evaluation
With automatic model evaluation, developers can seamlessly incorporate their own data or use curated data sets and predefined metrics such as accuracy, robustness, and toxicity. This feature eliminates the complexities of designing and running custom model evaluation benchmarks. The ease of evaluating models for specific tasks like content summarization, Q&A text classification, and text generation is a game-changer for developers looking for efficiency.
Human model evaluation for custom metrics
Amazon Bedrock also offers an intuitive human review workflow for subjective metrics like friendliness and style. Developers easily define custom metrics and use their data sets with just a few clicks. The flexibility extends to the option of leveraging internal teams as reviewers or opting for an AWS-managed team. This simplified approach eradicates the cumbersome effort traditionally associated with creating and managing human evaluation workflows.
Crucial details to consider
During the preview phase, Amazon Bedrock enables the evaluation and comparison of text-based large language models (LLMs). Developers can select one model for each automated evaluation job and up to two models for each human evaluation job using their own computers. Additionally, for human evaluation through an AWS managed team, custom project requirements can be specified.
Pricing is a crucial consideration, and during the preview phase, AWS only charges for the model inference required for evaluations, with no additional fees for human or automated evaluations. A full breakdown of Amazon Bedrock pricing is available to provide clarity on associated costs.
Our opinion
Amazon Bedrock model evaluation empowers developers, marking a significant leap in decision-making for base models. Automatic and human evaluation options, simplified workflows, and transparent pricing herald a new era in ai development. By delving into the preview phase, the industry anticipates the transformative impact on the artificial intelligence landscape. Developers, buckle up: the future of model selection is here.
