In our blog series, we’ve debunked the following observability myths so far: Part 1: You can skip monitoring and rely solely on logs Part 2: Observability is built exclusively for SREs Part 3: Observability is only relevant and beneficial for large-scale systems or complex architectures Part 4: Observability is always expensive In this post, we’ll tackle another fallacy …
Posted by Matt Barnes, Software Engineer, Google Research Routing in Google Maps remains one of our most helpful and frequently used features. Determining the best route from A to B requires making complex trade-offs between factors including the estimated time of arrival (ETA), tolls, directness, surface conditions (e.g., paved, unpaved roads), and user preferences, which …
Read more “World scale inverse reinforcement learning in Google Maps”
Today, we are excited to announce the simplified Quick setup experience in Amazon SageMaker. With this new capability, individual users can launch Amazon SageMaker Studio with default presets in minutes. SageMaker Studio is an integrated development environment (IDE) for machine learning (ML). ML practitioners can perform all ML development steps—from preparing their data to building, …
Read more “Amazon SageMaker simplifies the Amazon SageMaker Studio setup for individual users”
Generative AI is a type of machine learning and artificial intelligence (AI) that is garnering a lot of attention right now, and we want to help keep you a step ahead by sharing the latest training available from Google Cloud. In this blog, we have compiled resources to help you learn about generative AI, how …
Read more “Generative AI on Google Cloud: New training content, from introductory to advanced”
Posted by Edith Cohen and Uri Stemmer, Research Scientists, Google Research Differential privacy (DP) is a rigorous mathematical definition of privacy. DP algorithms are randomized to protect user data by ensuring that the probability of any particular output is nearly unchanged when a data point is added or removed. Therefore, the output of a DP …
In the rapidly evolving landscape of customer service, call centers are embracing cutting-edge technologies to elevate their operations and meet the growing expectations of today’s consumers. Among these transformative technologies, Generative AI chatbots have emerged as a game-changer. These intelligent virtual assistants are modifying the way call centers engage with customers, streamline processes, and deliver …
Read more “Top 10 Powerful Use Cases of Generative AI Chatbots in Call Centers”
We are in the midst of an AI revolution where organizations are seeking to leverage data for business transformation and harness generative AI and foundation models to boost productivity, innovate, enhance customer experiences, and gain a competitive edge. IBM and AWS have been working together since 2016 to provide secure, automated solutions for hybrid cloud …
Read more “Powering the future: The synergy of IBM and AWS partnership”
Posted by Edith Cohen and Uri Stemmer, Research Scientists, Google Research Differential privacy (DP) is a rigorous mathematical definition of privacy. DP algorithms are randomized to protect user data by ensuring that the probability of any particular output is nearly unchanged when a data point is added or removed. Therefore, the output of a DP …
Today, we are excited to announce that the Falcon 180B foundation model developed by Technology Innovation Institute (TII) and trained on Amazon SageMaker is available for customers through Amazon SageMaker JumpStart to deploy with one-click for running inference. With a 180-billion-parameter size and trained on a massive 3.5-trillion-token dataset, Falcon 180B is the largest and …
Read more “Falcon 180B foundation model from TII is now available via Amazon SageMaker JumpStart”
The pace of progress with AI model architectures is staggering, driven by breakthrough inventions such as Transformer, and by rapid growth in high-quality training data. In generative AI, for instance, large language models (LLMs) have been growing in size by as much as 10x per year. Organizations are deploying these AI models in their products …