The Beginner's Guide
Google Cloud Platform (GCP) for Machine Learning & AI
A brief introduction to Machine Learning & AI services offered by Google
Ranging from healthcare to finance to automation there is no such industry where Artificial Intelligence (AI) and Machine Learning (ML) is not transforming the world and giving ease of life to every individual.
But we can not imagine that AI and Cloud Computing are totally unrelated for a second. In 2020, more than 90% of companies are using cloud services for large data storage, big data analysis, managing data lakes, or data streaming, which are the initial core steps for any Machine Learning model.
Pay-as-you-go pricing model in cloud computing makes it easier for all of us to access services like GPU based computing servers and data lakes to manage large datasets from any cloud provider like Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure. AWS is undoubtedly leading the market with more than 60% market share public services but GCP is having a slight edge if we specifically talk about AI and ML cloud services.
In this blog, we will talk about a few out-of-the-box AI and ML services we can use in Google Cloud Platform (GCP).
Cloud AutoML
Cloud AutoML enables developers with limited machine learning and programming expertise to train high-quality models specific to their business needs. This service leverages Google’s proprietary research technology and also relies on Google’s state-of-the-art transfer learning and neural architecture search technology to achieve faster performance and more accurate predictions. People can train, evaluate, improve, and deploy custom machine learning models to solve problems for Vision, Translation, and Natural Language by using a simple graphical user interface within a few minutes. For more information on this, you can follow official documentation at https://cloud.google.com/automl/docs
Text-to-Speech-to-Text
Google provides very easy to use APIs to convert Text-to-Speech and Speech-to-Text with a few lines of code. These APIs are backed by many great key features including 90+ Wavenet voices, Voice and language selection, Global vocabulary, Pitch tuning, Noise robustness, Domain-specific models, Content filtering, Auto-detect language (beta), Automatic punctuation (beta), Text and SSML support, etc. This service is very useful for some different use cases like text bots, voice bots, transcribe multimedia content, customer service, voice generation, etc. These API’s are priced based on the number of characters sent to the service to be synthesized into audio or the amount of audio successfully processed respectively each month.
Dialogflow
Google Dialogflow is a development suite for creating chatbots and conversational IVR for websites, mobile applications, popular messaging platforms, and IoT devices. This tool is powered by Google’s machine learning and natural language processing algorithms to recognize a user’s intent, understand user sentiment, and extract prebuilt entities such as time, date, and numbers. Dialogflow supports 20+ languages and one-click integration with 10+ different platforms. Apart from this, it has some more key features such as automatic spelling correction, built-in analytics, and expand your bots to voice interactions. For more information, you can visit official documentation at https://cloud.google.com/dialogflow/docs.
AI Platform
Google AI Platform is a one-stop solution for machine learning developers and data scientists to take their ML projects from experiment to production and deployment. AI Platform integrated with several easy-to-use tools like BigQuery and Data Labeling Service to help you build and run your own machine learning applications quickly. You can store and manage the large amount of data with BigQuery and then prepare or label this data for model training using Data Labeling Service.
Wait…, not finished yet, AI Platform also supports Kubeflow to build portable ML pipelines and access to Google’s cutting-edge technology like TensorFlow, TPUs, and TFX tools to deploy your AI applications to production.
AI Hub
This is one of my favorites AI services provided by Google. AI Hub is Google Cloud’s hosted repository of plug-and-play AI components, end-to-end AI pipelines, and out-of-the-box ML algorithms. You can discover best AI content, pre-trained models, and a wide range of open datasets to further modify them for your custom needs. You can share your ML pipelines, notebooks, models, and other AI content via AI Hub. AI Hub also provides enterprise-grade sharing capabilities for organizations to privately host their AI content for users internally. For more information, you can visit official documentation at https://cloud.google.com/ai-hub/docs.
Tensorflow Enterprise
In the last but not least, Tensorflow Enterprise provides enterprise-grade support, performance, and managed services for your ML & AI workloads directly by the Tensorflow creators. TensorFlow Enterprise delivers so many out-of-the-box features like prioritized patches and bug fixes, fine-tuned TensorFlow containers, automatic provisioning, and scaling of resources like CPUs, GPUs, and TPUs, and managed services like AI Platform and Kubernetes Engine. And the best thing is it is available at no additional cost.
Before I leave, I strongly recommend everyone to give a try to Google Cloud AI and ML services. Google also provides $300 free credits to try their services. For more information, please visit https://cloud.google.com.
At crossML, we help companies and end customers with their digitalization journey provide by custom AI, Cloud, DevOps, and mobile solutions. Contact us for any type of solution or assistance regarding Cloud or AI at hello@crossml.com.
