The ability to create informed, data-defined decisions is having a huge impact on modern business. Data is quickly becoming the most valuable commodity around. The ability to harness the power of cloud computing to unravel billions of lines of unstructured data gives a business a distinct advantage over its competitors.
It is a common misconception that machine learning (ML) is a technology that requires highly skilled data scientists to make sense of huge volumes of business data. Google Cloud Platform (GCP) is breaking down barriers and delivering ML technologies to the masses with key, ready-made business solutions targeted at everyday users.
In this post, we will look at how GCP brings ML to the masses through premade APIs, AutoML, BigQueryML, and AI building blocks.
Vertical, end-to-end ML solutions
GCP has many ML solutions that target industry-specific end-to-end solutions. Boost sales with the Recommendations AI. Help your customers find what they are looking for with the Retail Search solution integrated into your website. Improve manufacturing quality and out with the Visual Inspection AI.
With Contact Center AI, automated agents are trained to answer everyday questions, such as what your opening hours are, freeing up your employees to deliver real customer service on more complex issues. How about leveraging Document AI to automate document processing, such as invoices, forms, and contracts. With its advanced capabilities like optical character recognition (OCR) and table parsing it can automate the laborious, manual data-recording tasks.
No/low-code ML solutions
The scarcity of professionals with ML skills has given rise to no-code or low-code solutions enabling a wider audience to exploit its benefits. Google also provides multiple solutions aimed at different use cases.
With BigQueryML you can execute ML models against your existing BigQuery datasets, resulting in substantial time and cost savings since there is no need to waste time exporting existing data for modeling purposes and then struggling with model deployment. Anyone who knows SQL can work on BigQueryML. Classification, regression, clustering, recommendations, anomaly detection and forecasting are the supported use cases.
ML is also great at making intelligent decisions on images, text, and videos. GCP’s AutoML is a no-code tool that provides services for the automation of tasks and the extraction of additional insights from unstructured data. For instance, with AutoML Text you can easily classify customer emails to create automated responses or extract addresses, names, order numbers, or any other entities from your documents. AutoML Image scans photos for object detection and image classification just asGoogle Photos used it to find your favorite cat photos. If you are interested in building domain-specific translation models or processing videos, you’ve got it covered with AutoML Translation or AutoML Videos, respectively.
The common requirement for any AutoML product is that you need to have training data to kick off the model learning. Apart from that, the service trains and deploys models with a few clicks and you just need to integrate it into your applications.
If you don’t have training data, you can leverage pretrained APIs for similar media types. They offer general solutions for common use cases, such as translation, speech-to-text, object or face detection, basic entity extraction, or detecting explicit content. Be aware that these services are not tailored to your needs. For example, the NLP API will be able to extract people’s names from documents but if you have a unique order number it won’t detect it. In such cases start with AutoML Text models.
Focus on ML not on infrastructure setup
GCP has its building blocks for advanced practitioners as well. Vertex AI, Google’s AI Platform, provides users with serverless services to enable data scientists to focus on modeling instead of setting up infrastructure. These solutions offer possibilities to submit training jobs and deploy models for serving in various fashions to mention just two. Leveraging the elasticity of Google Cloud and its ML-specialized accelerators, TPUs, makes it easy and cost efficient to train large-scale deep learning models in a matter of hours instead of days.
When it comes to ML Ops, Vertex is the go-to platform on Google Cloud to build production-ready ML systems. With the help of pipeline orchestration, metadata management, and data drift detection services, engineers can build reliable and trustworthy applications.
There is an ML solution for everyone. The hardest challenge is knowing what ML solutions are out there. You will be surprised at the ML routines available.
How Aliz.ai can help
Aliz.ai specializes in creating AI solutions for enterprise clients around the globe. Founded in 2010 by a team of business and IT professionals, we have one vision in mind: to help companies prepare for a new digital age.
Aliz was born in the cloud. Our team became pioneers in recognizing the role of big data and ML in business. We sought out the best engineers, and worked out a holistic, agile approach to our processes.