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Using Google's Speech-to-Text API with Python

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This post provides steps and python syntax for utilizing the Google Cloud Platform speech transcription service. Speech transcription refers to the conversion of speech audio to text. This can be applied to many use cases such as voice assistants, dictation, customer service call center documentation, or creation of meeting notes in an office business setting. It is not difficult to see the value this can bring to individuals and businesses. AWS has long been a leader in this space. Google, IBM, and Microsoft have of course developed their own services as well.


PyTorch on Google Cloud: How To train PyTorch models on AI Platform

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After creating the AI Platform Notebooks instance, you can start with your experiments. Let's look into the model specifics for the use case. For analyzing sentiments of the movie reviews in IMDB dataset, we will be fine-tuning a pre-trained BERT model from Hugging Face. Fine-tuning involves taking a model that has already been trained for a given task and then tweaking the model for another similar task. Specifically, the tweaking involves replicating all the layers in the pre-trained model including weights and parameters, except the output layer.


Siemens, Google Cloud to Collaborate on AI-based Solutions for Industrial Manufacturing

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NUREMBERG, Germany and SUNNYVALE, CA, USA, May 5, 2021 – Google Cloud and Siemens, an innovation and technology leader in industrial automation and software, today announced a new cooperation to optimize factory processes and improve productivity on the shop floor. Siemens intends to integrate Google Cloud's leading data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation solutions to help manufacturers innovate for the future. Siemens and Google Cloud to cooperate to transform manufacturing by enabling scaled deployment of artificial intelligence. Data drives today's industrial processes, but many manufacturers continue to use legacy software and multiple systems to analyze plant information, which is resource-intensive and requires frequent manual updates to ensure accuracy. In addition, while AI projects have been deployed by many companies in "islands" across the plant floor, manufacturers have struggled to implement AI at scale across their global operations.


Build &Deploy Machine Learning on Flask,AWS,Azure,Heroku,GCP

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Then this course is for you!! This course has been practically and carefully designed by industry experts to offer the best way of learning Data Science and Machine Learning the practical way with hands-on projects and Deployment throughout the course. This course will teach step-by-step how to deploy your machine learning models in the cloud as it is done in the industry. We will walk you through step-by-step on each topic explaining each line of code for your understanding. There is going to be a lot of fun, excited, and robust projects to better understand each concept under each topic.


The 23 Best Machine Learning Courses on Coursera for 2021

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The editors at Solutions Review have compiled this list of the best machine learning courses on Coursera to consider if you're looking to grow your skills. Machine learning involves studying computer algorithms that improve automatically through experience. It is a sub-field of artificial intelligence where machine learning algorithms build models based on sample (or training) data. Once a predictive model is constructed it can be used to make predictions or decisions without being specifically commanded to do so. Machine learning is now a mainstream technology with a wide variety of uses and applications.


10 Cool Cloud AI And ML Services You Need To Know About

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Google Cloud's machine learning-powered Document AI platform -- which already has been used to process tens of billions of pages of documents for government agencies and the lending and insurance industries among others -- became generally available last week, along with Lending DocAI and Procurement DocAI. The serverless Document AI platform is a unified console for document processing that allows users to quickly access Google Cloud's form, table and invoice parsers, tools and offerings -- including Procurement DocAI and Lending DocAI -- with a unified API. It uses artificial intelligence/machine learning (AI/ML) to classify, extract and enrich data from scanned and digital documents at scale, including structured data from unstructured documents, making it easier to understand and analyze. Doc AI solutions feature Google technologies including computer vision, optical character recognition and natural language processing, which create pre-trained models for high-value and -volume documents, and Google Knowledge Graph to validate and enhance fields in documents. Research and advisory firm Gartner predicts AI will be the top category that determines IT infrastructure decisions by 2025, driving a tenfold growth in compute requirements. Half of all enterprises will have AI orchestration platforms by 2025 to operationalize AI, according to Gartner, up from less than 10 percent in 2020.


Google Cloud lands Univision digital transformation pact

ZDNet

Here's a look at how the cloud leaders stack up, the hybrid market, and the SaaS players that run your company as well as their latest strategic moves. Google Cloud said Univision has signed a multi-year contract for artificial intelligence, machine learning and cloud services. Univision will also collaborate with other Google products such as YouTube, Android and the company's advertising and search tools. Media is a key vertical for Google Cloud and the strategic partnership with the Spanish-language media company highlights a few themes. First, Google Cloud is doing well by targeting core industries such as media, financial services and retail.


How to choose and deploy industry-specific AI Models

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As AI technologies become more advanced, previously cutting-edge -- but generic -- AI models are becoming commonplace, such as Google Cloud's Vision AI or Amazon Rekognition. While effective in some use cases, these solutions do not suit industry-specific needs right out of the box. Organizations that seek the most accurate results from their AI projects will simply have to turn to industry-specific models. There are a few ways that companies can generate industry-specific results. One would be to adopt a hybrid approach -- taking an open-source generic AI model and training it further to align with the business's specific needs.


Siemens, Google team to scale cloud AI in manufacturing

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Siemens has teamed with competitor Google Cloud to optimise factory processes and improve productivity on the shop floor with the mass deployment of machine learning applications. Siemens already has an industrial IoT cloud called Mindsphere but it intends to integrate Google Cloud's data cloud and artificial intelligence/machine learning (AI/ML) technologies with its factory automation systems. Many industrial manufacturers continue to use legacy software and multiple systems to analyze plant information, which is resource-intensive and requires frequent manual updates to ensure accuracy. In addition, while AI projects have been deployed by many companies in "islands" across the plant floor, manufacturers have struggled to implement AI at scale across their global operations. The combination of Google Cloud's data cloud and AI/ML capabilities with Siemens' Digital Industries Factory Automation portfolio, manufacturers will be able to harmonize their factory data, run cloud-based AI/ML models on top of that data, and deploy algorithms at the network edge.


Operationalizing AI to eliminate data siloes, train models and more

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Editor's note: Today's guest post comes from AI for healthcare platform Lumiata. Here's the story of how they use Google Cloud to power their platform--performing data prepping, model building, and deployment to tackle inherent challenges in healthcare organizations. If ever there was a year for healthcare innovation--2020 was it. At Lumiata, we've been on a mission to deliver smarter, more cost-effective healthcare since 2013, but the COVID-19 pandemic added new urgency to our vision of making artificial intelligence (AI) easy and accessible. Using AI in healthcare went from a nice-to-have to a must-have for healthcare organizations.