AWS Drives a New Paradigm with Correct Machine Learning Tools

By Srikanth
5 Min Read
AWS Drives a New Paradigm with Correct Machine Learning Tools 1

Over the previous 10 years, AWS sprang out as a ruler in the cloud computing space and carried on dominating the market. According to the recent market report, cloud revenue hiked up to $70 billion, the pious year and out of this, AWS cornered the largest share. Also, 2018 appeared to be a successful mark of the year, as cloud computing handed out accounting for more than 20 percent of the total IT budget for organisations.


Also a market report estimates that even though the shift towards multi-cloud deployments was profound, yet, the market leader AWS managed to stretch its dominance with nearly 35 percent of global cloud facilities by the end of the year, 2018.

During the sudden enhances, the continual shift to AI also led to the growth and enhancement of an intelligent cloud with top tier cloud establishes broadening their AI solutions. Analysts reckon AI in the cloud is anticipated to boost at a yearly rate of 50% through 2025. With cloud to achieve a critical AI enabler, the market leader then profound AI to be more accessible through its cloud services.

Such as the other technology majors, AI also was a natural progression with Amazon benefiting machine learning technologies to power appeal forecasting, product search rankings, warehouse fulfillment centers, inventory forecasting and fraud protection following the other use cases.

Shekar defines about Amazon that it has been an implementing machine learning, at scale for the previous two decades beginning from the soon, after recommendation system that was developed to counsel books for users to purchase on Amazon portal. “Since then, every aspect of what we do is customer focused, for example, the forecasting we do for our inventory in the warehouses to make sure your order reaches to you in two days. Practically every aspect there is ML-powered, for example, we use computer vision technology to recognize objects and ensure the right items are shipped to you,” claimed Shekhar in a report to analyticsindiamag.

Further, as AI became the neoteric normal, soon all the organisations started to focus on gathering accurate data, getting it organised and labelled. Therefore, this then proved to be a prolonged demand, while AWS’s cloud-based facilities, automates every step towards stage of the machine-learning pipeline with the build-train-deploy model with a low investment cost. “Sagemaker is our studio, it encompasses all of the things necessary for you to go ahead and build out a fully managed machine learning capability.

So, we started in 2017 by putting together the basic pieces which is the ability to build ML model, the ability to train them and then effectively go ahead and deploy and scale it out. Now, it is the best place to run all forms of AI including TensorFlow,” Shekhar added further.

Chasing the heights for accessible AI and learning gets vigorous, cloud computing giant AWS, such as the other tech majors launch cloud certifications and formal training programmes that also shades different product features. Among the most popular certifications, is Solution Architect that stretches for the gamut of features, to better recognise the growing side, operational side and most significantly security side.

In order to provide data science and machine learning to the developers, Amazon also stepped forward to launch Machine Learning University in November, 2018 by open sourcing its machine learning courses caused to warm up engineers at Amazon. As per the announcement, there are beyond 30 self-service, self-paced digital courses with more than 45 hours of courses, videos, and labs for four key groups: developers, data scientists, data platform engineers, and business professionals, knowingly.

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