No matter your IT role, Amazon Web Services has the machine learning tools and services that will meet your needs. AWS machine learning tools and services are specially designed to be workable for a deep level of expertise.

ML Services are driven by APIs, which means that developers can add intelligence to any application. AWS offers numerous pre-trained services that provide for chat and computer speech, vision, and even language analysis. AWS also supports all of the primary learning frameworks, including Caffe2, Apache MXNet, and Tensorflow, making it possible to bring or develop any model you want.

One of the major plusses of AWS machine learning is security. You can control access to resources with granular permission policies. AWS Storage and database services have strong encryption to make sure your data stays secure. You also benefit from the ability to choose whether you want to manage the encryption keys or let AWS manage them.

When it comes to analytics, you can choose from a broad set of data analysis services that include data warehousing, business intelligence, batch processing, and stream processing. You can even orchestrate your data workflows.

Another vital feature of AWS machine learning is that the machine learning services offered are integrated deeply. The integration is so deep with the rest of the platform, and that includes the data lake and the database tools you will need to run machine learning workloads.

Computing power is critical for machine learning. AWS provides a wide array of computing options for both training and inference for machine learning. There are powerful GPU-based instances as well as compute and memory-optimized instances. They even offer field-programmable gate arrays that allow one to create a digital integrated circuit programmed after manufacturing occurs. For example, an FPGA can be used by a computer used to tailor a microprocessor to their own specific needs.

Amazon Sagemaker is the newest managed platform that allows developers to build, train, and deploy machine learning models at scale with ease. Amazon Sagemaker removes much of the complexity that interferes with developer success with each of the Machine Learning steps. This is done by collecting and preparing training data and discovering which elements of your data set are essential to selecting an algorithm and framework. You then have to decide on your approach, teaching and training your model on making predictions and finally fine-tuning your model.

From platforms to services and tools for machine learning, AWS has you covered. It is comprehensive, secure, cost-efficient, and makes machine learning easy for everyone.

Dec 18 20
Christina Zumwalt
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