Cloud Services | Machine Learning

Getting Started with Artificial Intelligence, Machine Learning & Deep Learning Basics

It is no secret that Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are rapidly growing fields in the technology sector, but what exactly are they?

Artificial Intelligence (AI)

AI is any technique that enables computers to mimic human intelligence using logic, if-then statements, and ML (including deep learning).

Machine Learning (ML)

ML is the technology used to create AI systems today. ML builds models from large amounts of data which are used to make and validate decision logic, then feed input data as output for human-like predictions or classifications. ML can be used for more than AI, though most ML applications are AI related.

Deep Learning (DL)

DL is a type of ML that utilizes deep neural networks which simulate how the human brain works, enabling AI systems to process more complex tasks and scenarios which were formerly impossible.

WHY Machine Learning

Maximize Machine Learning Capabilities

Machine learning allows businesses to solve complex problems quickly and accurately with less manual labor than traditional methods.

Here are some key advantages of machine learning:

Increase Efficiency & Productivity

With machine learning, businesses can automate a variety of processes that traditionally require significant manual labor. By leveraging machines to process large volumes of data quickly and accurately, businesses can save time and energy while increasing productivity. This can be especially useful in industries such as finance and healthcare, where data is constantly changing, and new developments need to be accounted for quickly.

Make Accurate Predictions & Decisions

Machine learning algorithms use sophisticated mathematical models to process vast amounts of data in order to generate insights about potential outcomes or options for decision-making. This enables organizations to make highly accurate predictions about future events or scenarios and provides them with resources for better decision-making.

Reduce Operational Costs

Businesses today must remain agile in order to compete effectively in a global marketplace. With machine learning, firms can optimize their operations by reducing costs associated with manual tasks, such as administrative overhead or staff training expenses. The automation capabilities provided by ML provide organizations with greater flexibility in how resources are allocated while enabling more efficient use of capital investments such as staff hours or technology solutions.

Ensure Security and Compliance

Leveraging ML across security, risk, and compliance use cases is a fast-growing trend, especially in the financial services sector. Fraud detection models help keep consumer data safe and prevent malicious attacks against bank accounts or mobile endpoints. On the other end of the spectrum, ML automates mundane tasks such as financial-document analysis, reducing manual effort and allowing the workforce to focus on higher-value tasks.

Improve Customer Experience

Using machine learning algorithms, businesses can collect consumer data from sources such as loyalty programs or surveys in order to gain a richer understanding of customer behaviors and preferences. As a result, companies are able to enhance their customer experience by providing tailored services or products that meet the needs or desires of individual customers.

Enable Product and Service Innovation

Businesses are already using ML pervasively to enable product and service innovation. They inform their product roadmap through intelligence extracted from customer feedback; drive the product development lifecycle, including DevOps and quality assurance through automation and intelligence; and infuse ML capabilities directly into new products that benefit the end user.

Customer Success Story

CareAR realized the importance of a smooth technology solution to ensure patients receive the best care possible. Our cloud team is hard at work creating an architecture that will provide CareAR with the necessary telemetry.

Curious about how we use AWS to deliver real-time data and machine learning? Read our case study!

ML challenges: what are they?

Challenges of Adoption

ML is frequently the catalyst that turns business data into accurate predictions and actionable information, but as with many emerging technologies, there are challenges to adoption, including data ambiguity, complexity, cost, and lack of skills.

Data Ambiguity

Businesses can struggle with various issues related to data. First and foremost, many are unaware of all their possible data sources that might hold hidden insights. Even when they’ve identified data, there’s a lack of labeled data ready for ML. Furthermore, even labeled data can prove to be an issue where integrity is in question since data can often have hidden biases based on human labelers. Finally, businesses often struggle with ensuring the right data management and governance policies are in place to allow the right people and processes to access, store, and manage the data securely.

Complexity

The ML workflow can be time-consuming and iterative, which leaves many organizations and developers thinking ML is complex and difficult to use. There are many steps involved, from prepping data and choosing algorithms to building, training, and deploying models…and iterating over and over again. There are decisions to be made about infrastructure—selecting the right compute for training and inference, considerations for cloud, on-premises, and edge deployments.

Cost Inhibitors

ML training and inference can be expensive, especially since models require iterations to improve the accuracy of predictions. Because embarking on ML initiatives is new to many companies, they also don’t have the experiences or skills in-house and often have to rely on costly external resources to kick-start projects.

Lack of Skills

Even when companies embrace new technologies like ML to drive business transformation, having the right skills is often a road blocker to getting started. ML initiatives require ML expertise to build and train ML models— this includes the skills of ML developers, data scientists, and researchers to build algorithms and train models. These skills are not in great supply and are often unavailable in-house.

3 steps to modernization

How can your business become more data-driven?

For businesses that want to become more data-driven, there are three steps to modernizing your data infrastructure.

data driven steps

Modernize Data Infrastructure: First, move from an on-premise database to a cloud infrastructure.
Liberate Data: Second, break down data silos and increase accessibility by transitioning to a lake house architecture.
Innovate with ML: Third, use AI and machine learning to unlock insights from the collected data.

With these steps, your business can take advantage of big data opportunities.

Understand Business Objectives

It is important for both business and technical leaders to understand the benefits of adopting machine learning, particularly those related to your organization. We can help you identify these objectives, so you can reap the rewards of success with ML. Our team will provide support so that decision-makers recognize the pivotal part they have to play in organization ML adoption.

Advance your Data Strategy

Data is gold for leaders who are looking to disrupt their industries with ML. But many organizations don’t have ML-ready data. Our cloud team can help your organization get ready for machine learning adoption with our data collection and use plan, designed to work even at the proof-of-concept (PoC) stage. We'll also evaluate your data for quality and usefulness and clean and label it correctly for machine learning models, so you get valuable insights from the results.

Leverage the Cloud

Our cloud experts know how to use the right tools to get the most out of your ML initiatives. With AWS cloud, organizations can access reliable data storage, security, analytics services, and compute resources. Our big data team has a wealth of experience working with Amazon SageMaker, Amazon Pinpoint, and Amazon Rekognition, to name a few of the many AWS services that benefit businesses.

Cloud computing offers numerous advantages, such as speed, scalability, and cost-efficiency - plus access to high-performing CPU and GPU processors is essential for vast training projects and deployment. Data Lakes on the cloud make ML activities much simpler and quicker to expand or repeat.

Enable your Organization

Our cloud team and your subject matter experts can join forces to craft a successful proof-of-concept strategy. We'll define a process that brings scientists, developers, business stakeholders, and other experts together in order to set your project up for success. So let’s get started!

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AWS has the most serverless options for your data analytics in the cloud, including options for data warehousing, big data analytics, real-time data, data integration, and more. AWS manages your organization's underlying infrastructure so you can focus solely on your application.

Machine Learning (ML) Integration

AWS analytics services leverage proven machine learning (ML) and natural language capabilities to help you gain deeper and faster insights from your organization's data.

Ingest Data from Any Source

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Gain Insights from your Data

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AWS-powered data lakes, supported by the unmatched availability of Amazon S3, can handle the scale, agility, and flexibility required to combine different data and analytics approaches. Build and store your data lakes on AWS to gain deeper insights than traditional data silos and data warehouses allow.

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