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AI & Machine Learning FAQ
The cloud lets you store the terabytes of data needed to "train" a machine learning model to look for the patterns you specify and respond appropriately. Ditto on your working data. Only in the cloud can you store the volumes of operational data your enterprise will generate. The cloud easily lets you distribute your data across multiple nodes, to ensure your data remains available even in failure scenarios.
Having your data in the S3 cloud offers additional benefits as well. First, the pay-per-use model works well with the computational workloads AI or ML requires, and can be scaled up or down easily. Many cloud providers, Amazon included, offer many machine learning options that don't require advanced skills in AI or ML, lowering the learning curve and saving development costs.
Artificial intelligence (AI) is an umbrella term for the science of imitating human abilities. Machine learning is a branch of AI that trains machines how to learn (or develop a model), based on patterns. The larger the set of data from which those patterns are derived, the more accurate the models are.
The ability to recognize patterns and anomalies has led to MLs application in a wide range of fields. Fraud and anomaly detection are just the beginning. AI and ML have been successfully applied in fields as diverse as technical support, business intelligence, automotive, banking, government, and retail. Anywhere you can imagine groups of humans picking through data, AI and/or ML can do the job.
Alooma lets you pipe and access your big data at scale in S3 buckets, for ML training, analysis, and more!
Persistent, scalable, format-agnostic data storage remains one of the biggest challenges for any team to get started with machine learning or AI. Since a lot of machine learning projects are run against very large sets of data, it makes sense that for both training and analytics that data stores must be very large, and able to auto-scale.
Of course AI or ML mean nothing if only a few experts can use it. In order to really leverage their capabilities in production, users must be able to access state-of-the-art ML and AI capabilities without a learning barrier. That means these functions must not only be available, but also ready to use.
Machine learning combines the power of computing with humans' ability to study and understand patterns from data itself. A machine will find an anomaly, derive a pattern, or respond to a condition across millions — or even billions— of records faster and more reliably than a group of humans ever could.
With AI or ML, it's easier to delve into vast quantities of unstructured data including text, images, videos, or graphs to find patterns, perform analysis, or perform actions based on specific conditions.
Almost any deterministic workflow that has taken teams of humans to do in the past is a candidate for automation or enhanced processing via ML or AI.