Empowering HR Solutions with Amazon SageMaker: A Case Study from Indeed.AI
Today, we are excited to bring you a blog post that was co-written with Ethan Handel and Zhiyuan He from Indeed.com. Indeed is the world’s #1 job site and a leading global job matching and hiring marketplace. Their mission is to help people get jobs, and they serve over 350 million global Unique Visitors monthly across more than 60 countries. Since their founding nearly two decades ago, machine learning (ML) and artificial intelligence (AI) have been at the core of building data-driven products that better match job seekers with the right roles and get people hired.
In this post, we will delve into how the Core AI team at Indeed is utilizing Amazon SageMaker to accelerate their AI research, development velocity, flexibility, and overall value in their pursuit of leveraging advanced large language models (LLMs).
Infrastructure Challenges and Solutions:
Indeed generates an enormous amount of data daily, making their business fundamentally text-based. To enhance performance on particular tasks or domains, the Core AI team evaluated if Indeed’s HR domain-specific data could be used to fine-tune open source LLMs. They chose Amazon SageMaker to address the challenges of efficient fine-tuning, serving production traffic, and enabling a variety of production use cases with flexibility.
Accelerating Fine-Tuning Using Amazon SageMaker:
By transitioning to Amazon SageMaker, the Core AI team was able to optimize resource utilization, reduce costs associated with idle resources, and simplify the process of setting up and managing training jobs efficiently.
Smoothly Serving Production Traffic Using Amazon SageMaker Inference:
The team standardized request and response formats for different models and built an inference infrastructure on Amazon SageMaker to host fine-tuned models. This setup enabled rapid iteration and could handle up to 3 million requests per day.
Serving a Variety of Production Use Cases with Flexibility Using Amazon SageMaker Generative AI Inference Components:
Amazon SageMaker’s inference components allowed Indeed to deploy different models on the same instance, optimizing resource usage and reducing latency. By dynamically scaling each model based on demand, they achieved significant cost savings and efficiency in model deployment.
Core AI’s Business Value from Amazon SageMaker:
The seamless integration of Amazon SageMaker inference components has accelerated Indeed’s path to value, enabling them to swiftly deploy and fine-tune models while benefiting from scalability and cost-efficiency. They have fine-tuned over 75 models, achieved performance benefits, and served 6.5 million production requests.
Indeed’s Contributions to Amazon SageMaker Inference:
Indeed has partnered with the Amazon SageMaker inference team to enhance generative AI capabilities within Amazon SageMaker, providing valuable inputs to improve offerings and empower other AWS customers to unlock the transformative potential of generative AI.
Conclusion:
Indeed’s implementation of Amazon SageMaker inference components has solidified their position as an AI leader in the HR industry, enhancing their ability to develop and deploy AI solutions tailored to their industry’s specific needs. The flexibility and scalability of Amazon SageMaker have empowered Indeed to continually adapt its AI-driven solutions and drive efficiency and innovation.
In conclusion, the partnership between Indeed and Amazon SageMaker has led to significant advancements in the field of AI-driven HR solutions, ultimately benefiting job seekers and employers worldwide. The Core AI team’s innovative use of Amazon SageMaker has not only improved their internal processes but has also contributed to the development of enhanced generative AI capabilities within Amazon SageMaker for the broader AI community.