Google most certainly raises eyebrows when it comes to AutoML. In May 2021, when the tech giant announced the general availability of Vertex AI – which brings AutoML and AI platforms together into a unified API, client library and user interface – it changed the way enterprises look at deploying and maintaining AI models for the better. The software requires nearly 80 per cent fewer lines of code to train a model versus competitive platforms. Thus, enabling data scientists and ML engineers across all levels of expertise to implement machine learning operations to efficiently build and manage AI/ML projects throughout the entire development lifecycle. Fast-tracking the AI/ML production in the wake of shifting market dynamics, Vertex AI brings together the Google Cloud services for building ML under one unified UI and API, simplifying the process to building, training, and deploying AI/ML models at scale.

Artificial Intelligence (AI) and Machine Learning (ML) are propelling advanced business solutions across industries. Algorithm-based machine learning development services are overcoming the business challenges of processing heavy data volumes. With accuracy and efficiency, cloud technologies like Google cloud AutoML are encouraging the development of dynamic machine learning solutions. At Oodles, we are testing the model performance of AutoML Natural Language to build domain-specific solutions for global businesses. Let's explore how cloud AutoML for machine learning solutions is triggering automation beyond human intelligence.

At Next '18 this past July, we announced a range of updates to our AI and machine learning offerings aimed at making AI more accessible to developers. With the excitement of Next behind us, we thought we'd share a little more on these updates and how they can help you quickly and easily inject AI into your applications. Cloud AutoML is a suite of machine learning products that leverages Google's state-of-the-art transfer learning and neural architecture search (NAS) technology so you can easily train high quality custom models, even if you have limited experience with machine learning. This delivers the best of both worlds: high model quality and ease of use. This new suite of products aligns with our mission to democratize AI, and make it easy, fast and useful for all developers and enterprises.

Artificial intelligence has become a sort of secret weapon in the battle to build the best cloud service platform. Google Cloud Platform is currently the underdog, trailing both Amazon Web Services and Microsoft Azure. But Google is betting robust AI will give it the edge it needs to catch up. At the annual Google Cloud Next conference which kicked off July 24 in San Francisco the company unveiled a series of AI-based product releases and enhancements for its analytics and machine learning tools, additional applications on G Suite, and new IoT products. Earlier this week, Google parent company Alphabet reported its Q2 earnings, which were ahead of Wall Street's expectations.