Transform Your Organization with Artificial Intelligence & Machine Learning

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!

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.

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!