Machine Learning with Amazon Web Services
The advancements and adoption of AI on a consumer level will create about 2.3 million Machine Learning(ML) jobs by 2020. Amazon’s ML services will be serving a large proportion of these activities, 85% of TensorFlow projects in the cloud happen with AWS, and 81% of deep learning projects in the cloud run on AWS, with these figures in mind one can be rest assured that AWS ML services will only grow popular with time. With a healthy competition from Google’s GCP and Azure, AWS products and services will be top notch.
One can choose from multiple ML frameworks like TensorFlow, PyTorch, Apache MXNet giving the user maximum flexibility. The service that is used in running these frameworks in the Amazon SageMaker, where developers and data scientists can Build, Train and Deploy their ML models. All these with the proven scalability, reliability and cost effectiveness of the AWS platform. Checkout their latest featured and offerings at this link https://aws.amazon.com/sagemaker/
You can start on the path of Machine Learning and other popular implementations by starting with one of our AWS training courses, which provides an in depth knowledge of the different AWS services, like Simple Storage Service, Elastic Cloud Computing, Virtual Private Cloud and many more. If you are already familiar with the AWS platform you can simply start by doing of many free tutorials and guides provided by AWS. Jump in and be part of the digital world that is dominated by AI and Machine Learning. Training at Spectrum will provides easy structure in Machine Learning with Amazon Web Services.
