In machine learning, more is always bound to be more. For instance, training on more data means more correct models.

At AWS, they carry on attempting to allow builders to build cutting-edge technologies quicker in a secure, trustworthy, and scalable manner. Machine learning is one such transformational technology that is top of mind for CIOs and CEOs, as well as developers and data scientists.

Hi Alexa (Part I)
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Last re Invent, the problem of authoring, training, and hosting ML models was rendered easier, faster, and more reliable when they launched Amazon SageMaker. Thousands of clients are trying Amazon SageMaker and building ML models on top of their data lakes in AWS at the moment.

While building Amazon SageMaker and applying it for large-scale machine learning problems, they understood that scalability is one of the essential aspects that they need to pay much attention to.

Therefore, as designing Amazon SageMaker, they accepted a good deal of challenge: to create machine learning algorithms that can manage an endless amount of data. What does that even mean though? No customer holds such an infinite amount of data.

However, for many customers, the amount of data that they own is undifferentiated from boundless. Bill Simmons, CTO of Dataxu, declares, "We process 3 million ad requests a second - 100,000 features per request. That's 250 trillion ad requests per day. Not your run-of-the-mill data science problem!".

Portrait of a lifeless Alexa –In this picture, she said hi!.
Photo by Andres Urena / Unsplash

For these customers and many more, the conception of "the data" does not come into existence. It's not static. Data remains being accrued at all times. Their response to the inquiry "how much data do you have?" is "how much data can you handle?"

To make things even more challenging, a system that can handle a single big training job isn’t almost great enough if training jobs are dawdling or costly. Machine learning models are often trained tens or hundreds of times.

During evolution, many various versions of the anticipated training job are run. After that, to select the best hyperparameters, many training jobs are run simultaneously with somewhat different configurations.

To that end, Amazon SageMaker proposes algorithms that train on indistinguishable-from-infinite amounts of data both rapidly and cheaply. This sounds like a pipe dream. However, this is bound to be what they set out to do.