"With big data comes big responsibilities" could be the motto hanging above any data scientist’s door — especially now that machine learning (ML) and artificial intelligence (AI) have taken root in the business landscape and will likely dominate far into the future.
A recent report from O’Reilly indicated that 51% of organizations are now either early adopters or sophisticated users of ML, turning to it for everything from support chatbots to self-guided finance apps, autonomous vehicles, IT operations, and even medicine. In fact, 59% of executives say that machine learning and AI will improve their company’s use of data, according to research from PwC.
The predictions gleaned from ML can benefit every industry without exception. This all but ensures that data science will play an important role in business for a long time to come — especially as analysts project that the global data creation (various structured, unstructured, and semi-structured forms) will grow 10-fold from today’s levels by 2025.
But the degree to which data science methods, algorithms, and processes are used by organizations like yours to keep pace with the future demands of ML and AI depends almost entirely on getting access to more and better data — which depends, in turn, on the power and capability of the tools in your data science stack.
What exactly is the data science stack?
A data science (DS) stack today has to help you store, analyze, source, process, and visualize data in its totality. But what constitutes a DS stack? Typically, DS stacks will include your business and product data sources, an extract/transform/load (ETL) tool, a data warehouse, and at least one data analytics tool. Like other tech stacks — those for marketing, for sales, or commonly used in other functional groups — a DS stack will vary greatly, both in terms of the number and types of tools, from business to business.
Regardless of your current stack, extracting maximum value from ML and AI means the stack will need to evolve to accommodate ever-expanding data volume and complexity. And it’ll have to enable you to respond quickly and decisively to predictions — all with less effort, not more.
With a solution like Amazon S3, you can fuel your data science stack with a singular data storage and analytics system of near-infinite scale to deploy ML and AI capabilities without having to invest in additional ML technologies that can stress current resources.
Going serverless with S3
Security, flexibility, compliance, and scalability are the hallmarks of any cloud-based business solution and comprise the non-negotiables of just about any data strategy today.
But even in the cloud, data operations and management often require multiple tools for collection, transformation, storage, and analysis, with workloads and functionality often distributed across many servers and systems. Adding ML to the mix — without taking a hard look at where greater efficiencies can be gained in your data systems — can slow down and complicate what should be a streamlined undertaking with metamorphic results.
Reducing the complexity of how your data is stored and processed can help you more easily integrate ML into your larger data strategy and reap its rewards faster. In response, companies are increasingly turning to cloud-exclusive storage for their essential data to minimize the number of systems they have to manage and to take advantage of faster data transmission, lower latency connections, and greater agility cloud solutions offer.
One of the best cloud storage platforms to emerge in recent years is Amazon S3’s object storage platform. Amazon’s solution stores all your data from the web, in any amount, that you can then query at any time to create your own machine learning models and begin receiving predictions. S3 allows you to:
- Copy and upload your data from Amazon Redshift or Amazon Relational Database Service to S3, or use an ETL tool to extract, transform, and load your data from all your data sources
- Query your data in place — right from S3 — without having to move it to another analytics system
- Build, test, and run your own machine learning models or use Amazon ML as a guide
- Access your input files to evaluate your machine learning models and generate batch predictions
- Send batch prediction files to an S3 repository that you specify
Additionally, all the other benefits of the cloud apply. S3:
- Offers the highest standards in security and compliance
- Enables extensive scalability through a vast global infrastructure
- Allows you to integrate with other software and services from Amazon’s network of vendor partners
- Provides for flexible management and administration, along with easy data transfer
But perhaps most importantly, S3 enables "serverless ML" because you do not have to purchase or provision servers to specifically house your ML code, thereby eliminating the need to manage additional infrastructure. Instead, you can get your ML predictions through simple APIs. And it is this inherent simplicity and agility that allows you to pivot quickly, achieve outcomes faster through ML, and make transformative business decisions.
An integral part of the solution
Enterprises use Alooma to collect data from disparate sources and transform it, including standardizing, input mapping, cleansing, customizing, and enriching data, before loading it into S3. Once the data is in S3, companies can then point their ML tools at the repository and run their models.
In essence, Alooma bridges the distance between where your data currently sits to its potential in ML contained within S3, allowing you to gain access to your own data — from as many data sources, in as many formats and types as it exists, and with minimal effort — so that you can utilize ML and AI more effectively.
As ML and AI increase the demand for timely, if not real-time, access to data and far more powerful computing capabilities than we’ve seen before, your data science stack isn’t complete without S3’s object storage and analysis together with Alooma’s data integration capacity.