Machine Learning (ML) is all about “letting machines make better decisions for us.”
Machine learning as the application of artificial intelligence has revolutionized the world of technology. The field of cloud computing is not untouched by the importance of machine learning. With the cloud, it is possible to digest, ingest, and analyze huge amounts of data at scale and machine learning takes all this data and makes predictions, learnings, and acts according to it.
Benefits of Machine Learning in the cloud
Cloud’s pay-as-you-go model is ideal for bursty machine learning workloads. This allows the businesses to leverage the power and speed of graphics processing units (GPUs) without the hardware investment. The scalability of cloud services makes it easier for companies to experiment with machine learning capabilities and scale up as project demand increases or when it goes into production.
Furthermore, the cloud makes intelligent capabilities accessible without the need to offer advanced skills in data science or artificial intelligence. The Google Cloud Platform offers many machine learning options that don’t require a thorough knowledge of machine learning, artificial intelligence, or a team of a data scientist.
To add, there is no need to use a cloud service provider for building a machine learning solution since there are many open-source machine learning frameworks that companies can run on their own hardware.
Machine learning on Google Cloud Platform
Machine learning has been a basis for Google’s internal systems for many years. Google cloud users across industries from healthcare and financial services to retail, media and entertainment and web services now have enormous machine learning models at their disposal due to the growing number of cloud services and intelligent apps.
Google Cloud Platform (GCP) has a range of tools/products for users, both beginners , and experts. Machine learning on Google Cloud Platform has provided a deep and unique understanding of techniques, right frameworks, data, and infrastructure for data-driven systems on a big scale in order to automate customer’s requirements.
Features of Google Cloud Machine Learning
The special features of Google Cloud ML make it easier and more convenient to manage and design. Let’s take a look at them-
Integration– Google Cloud ML is associated with data flow which provides a processing feature besides the cloud storage.
Online prediction and training support in terms of multiple frameworks– The multiple frameworks are used to predict and train models. Clustering and classification are also there in multiple frameworks.
Server-side pre-processing– the data can be shared in raw format so as to reduce local computation. It also supports prediction and training.
Automatic resource provisioning– in case businesses don’t have the infrastructure in terms of both software and hardware, they can still develop and deploy ML models. In automatic provisioning, the resources can be acquired on demand.
Besides the above, Google Cloud Machine learning Engine use hyper-parameters with HyperTune and also offers portable models.
Machine Learning on GCP can empower businesses to take crucial decisions faster
Google Cloud offers a pool of machine learning API’s that can help businesses innovate faster. The data can be transferred into actionable insights.
Visualize and explore data according to your requirements– Businesses can turn their data into compelling visuals with a community built, out-of-the-box, or customized visualizations.
Create a collaborative data-driven culture– By activating data-insights at each level, the business can bring about a highly-collaborative culture of data-driven decision making. With tools available on the Google cloud platform, teams can instantly start collaborating over big-data. Besides setting in reports and dashboards into websites or internal apps, developers can securely insert analytics into the customer-facing apps.
Predictive analysis- Building machine learning models is made easy with Google Cloud Platform, businesses can use familiar SQL to build and train ML models over huge datasets.
Businesses that want to lead the pack to opt for Google cloud since it helps in improving the web services, there is scalability, and enables the use of rich data to power advanced analytics. In other words, it gives businesses stems from bringing innovative solutions easily accessible. So when businesses are ready to move to the next level, ML can be trained to assist in decision making and also help in identifying important facts from the data automatically. The various machine learning API-interfaces can help businesses to analyze text, language, and visual data.
So whether your business is in an early stage of its journey or well established, the embedded machine learning capabilities in GCP can help you make crucial business decisions faster.
Take Yourself through the insights of Machine Learning here – Mastering Machine Learning and Deep Learning with TensorFlow