ã€Global Science and Technology Report】 Recently, the Google Cloud Next 2017 conference was held in the United States. At this conference, Google Cloud announced many new advances in machine learning. Many of these initiatives have lowered the threshold of artificial intelligence. These initiatives include the release of video intelligence APIs; the announcement of wider availability of machine learning engines; the establishment of advanced solution laboratories, the provision of machine learning expert services, and the acquisition of Kaggle, a data science community, which has reduced computing, algorithms, data, talent, etc. The threshold of artificial intelligence allows more companies and individuals to have the opportunity to use artificial intelligence to create unlimited possibilities and jointly advance the progress of artificial intelligence.
At the conference, Google first announced the acquisition of Kaggle, a data science community. Kaggle is the world's largest community of data scientists and machine learning enthusiasts. More than 800,000 data professionals use Kaggle to explore, analyze and master the latest developments in machine learning and data analysis. Google Cloud will provide the most advanced machine learning environment for this huge community and provide opportunities for a direct market model.
Publish video intelligence API to identify video content
The Google Cloud Video Intelligence API uses a powerful deep learning model based on the TensorFlow architecture for large-scale media platforms like YouTube. This API is also the first API that enables developers to easily search and discover video content. Developers can provide relevant entities (such as dogs, flowers, people and other nouns, and verbs such as running, swimming, and flying) in video content. Information completes the search. When these entities appear, this API can even provide context understanding. For example, a search for "Tiger" will find all the precise shots of all tigers stored in the Google Cloud video collection.
Google has long been working with the world's largest media company, helping them discover value from unstructured data such as video. This API is for large media companies and technology consumer companies that want to build media indexes or seek simple ways to manage crowd-sourced content, but also for similar Cantemo, etc. They want to implant it into their own video management software. In the partner.
With the newly released video intelligence API, Google Cloud Machine Learning has added a growing set of APIs: Vision, Video Intelligence, Speech, Natural Language, and Translation. (Translation) and Jobs. These APIs enable customers to build next-generation applications that can see, hear, and understand unstructured data, greatly expanding the scope of machine learning in many areas such as next-generation product recommendation, medical image analysis, and fraud monitoring.
Machine learning engine is more widely used
Google Cloud's machine learning engine in Georgia attracts companies and organizations that want to train their own models and deploy them to cloud production. The Google Cloud Machine Learning Engine has become an advantageous management service that can build a customized machine learning model based on TensorFlow that can interact with all types and sizes of data. In addition, it also integrates the complete data analysis product line of Google Cloud platform, including Cloud Dataflow, Cloud Datalab and Google BigQuery.
Google also partners with technology partners to help them better solve practical problems with the Google Cloud Machine Learning Engine. Two recent examples include: SpringML uses this service to provide end-users with instant analysis; SparkCognition uses it to identify and block zero-day attacks.
Establish advanced solution labs and provide machine learning expert services
Google’s Advanced Solution Lab provides customers with a dedicated facility that allows customers to work directly with Google’s machine learning experts to help customers achieve the most pressing challenges through machine learning. Based on the TensorFlow and Google Cloud machine learning engines, customers can explore specific business use cases and establish a solid foundation in machine learning.
Explore data through Cloud Datalab
Cloud Datalab is an interactive data science workflow tool that makes it easier for developers and data scientists to explore, analyze, and visualize data in BigQuery, Cloud Storage, and local storage. For the development of machine learning, they can take a life-cycle approach: build a model prototype on a small, locally stored data set, and then use the complete data set to train in the cloud.
Google Cloud hopes that companies can use machine learning to promote their business development, and also welcomes companies seeking solutions from Google Cloud. At the same time, Google Cloud is also particularly interested in the innovative use of APIs and is looking forward to receiving amazing feedback.
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