Posts in tag

Machine Learning


Google Cloud’s Dataflow recently announced the General Availability support for Apache Beam’s generic machine learning prediction and inference transform, RunInference. In this blog, we will take a deeper dive on the transform, including: Showing the RunInference transform used with a simple model as an example, in both batch and streaming mode. Using the transform with …

Artificial intelligence (AI) can automatically learn patterns that humans can’t detect, making it a powerful tool for getting more value out of data. A high-performing model starts with high-quality data, but in many cases, datasets have issues such as incorrect labels or unclear examples that contribute to poor model performance. Data quality is a constant …

The data received at serving time is rarely in the format your model expects. Numerical columns need to be normalized, features created, image bytes decoded, input values validated. Transforming the data can be as important as the prediction itself. That’s why we’re excited to announce custom prediction routines on Vertex AI, which simplify the process …

Enterprises across many industries are adopting artificial intelligence (AI) and machine learning (ML) at a rapid pace. Many factors fuel this accelerated adoption, including a need to realize value out of the massive amounts of data generated by multichannel customer interactions and the increasing stores of data from all facets of an enterprise’s operations. This …

One of the largest telecommunications companies in the world, Vodafone is at the forefront of building next-generation connectivity and a sustainable digital future. Creating this digital future requires going beyond what’s possible today and unlocking significant investment in new technology and change. For Vodafone, a key driver is the use of artificial intelligence (AI) and …

ML-driven innovation is fundamentally transforming computing, enabling entirely new classes of internet services. For example, recent state-of-the-art lage models such as PaLM and Chinchilla herald a coming paradigm shift where ML services will augment human creativity. All indications are that we are still in the early stages of what will be the next qualitative step …

This is part one of a two-part series with practical tips to start your AI/ML journey. Machine learning (ML) and artificial intelligence (AI) are creating more personalized and easier digital experiences for constituents. According to recent studies, 92% of U.S. citizens1 report that improved digital services would positively impact their view of government. At the …

When you build a machine learning product, you need to consider at least two MLOps scenarios. First of all, the model could be replaced later, as breakthrough algorithms are introduced in academia or industry. Secondly, the model itself has to evolve with the data in the changing world. We can handle both scenarios with the …

One of the main challenges machine learning practitioners face is the availability of annotated training datasets or a lack thereof. In many cases, practitioners may have access to existing datasets that have been manually extracted, which they can use to accelerate their model training. In this post, we demonstrate how Google Cloud AI/ML products can …

Artificial intelligence (AI) and machine learning (ML) are transforming industries around the world, from trailblazing new frontiers in conversational human-computer interactions and speech-based analysis, to improving product discovery in retail,to unlocking medical research with advancements like AlphaFold. But underpinning all ML advancements is a common challenge: fast-tracking the building and deployment of ML models into …