In 2016, we launched AWS Shield, a managed Distributed Denial of Service (DDoS) protection service that safeguards applications running on AWS. AWS Shield provides always-on detection and automatic inline mitigations that minimize application downtime and latency without needing to contact AWS Support. There are two tiers of AWS Shield: Standard and Advanced. All AWS customers benefit from the automatic network layer protections of AWS Shield Standard and at no cost. AWS Shield Standard defends against the most common, frequently occurring network and transport layer (Layer 3 and 4) DDoS attacks to maximize the availability of AWS services. For customized protection against sophisticated (Layer 3 to 7) threats targeting your applications, you can subscribe to AWS Shield Advanced. AWS Shield Advanced…
New AWS Scholarship Program Helps Underrepresented and Underserved Students Prep for Careers in AI and ML
As a woman working in information technology (IT) for many years, it has always been close to my heart to challenge long-standing gender stereotypes and inspire more young learners to consider a career in tech. With artificial intelligence (AI) and machine learning (ML) defining the future of technology, this future also depends on diverse representation. The World Economic Forum estimates that technological advances and automation will create 97 million new technology jobs by 2025, including in the field of AI and ML. Yet, according to their research, women make up just 32% of AI jobs globally. The Pew Research Center found that Black and Hispanic workers in the U.S. comprise just 9% and 8% of workers in the science, technology,…
Now in Preview – Amazon SageMaker Studio Lab, a Free Service to Learn and Experiment with ML
Our mission at AWS is to make machine learning (ML) more accessible. Through many conversations over the past years, I learned about barriers that many ML beginners face. Existing ML environments are often too complex for beginners, or too limited to support modern ML experimentation. Beginners want to quickly start learning and not worry about spinning up infrastructure, configuring services, or implementing billing alarms to avoid going over budget. This emphasizes another barrier for many people: the need to provide billing and credit card information at sign-up. What if you could have a predictable and controlled environment for hosting your Jupyter notebooks in which you can’t accidentally run up a big bill? One that doesn’t require billing and credit card…
Announcing Amazon SageMaker Inference Recommender
Today, we’re pleased to announce Amazon SageMaker Inference Recommender — a brand-new Amazon SageMaker Studio capability that automates load testing and optimizes model performance across machine learning (ML) instances. Ultimately, it reduces the time it takes to get ML models from development to production and optimizes the costs associated with their operation. Until now, no service has provided MLOps Engineers with a means to pick the optimal ML instances for their model. To optimize costs and maximize instance utilization, MLOps Engineers would have to use their experience and intuition to select an ML instance type that would serve them and their model well, given the requirements to run them. Moreover, given the vast array of ML instances available, and the…