A look at how automated processes are changing the health landscape
By Robert Barresi, Enterprise Architect
With so many cloud service offerings and providers on the market, Federal Government health agencies can find it difficult to know where to begin when it comes to investing in cloud and AI/ML. In the first two installments of this series, we talked specifically about Disaster Recovery as a Service (DRaaS) and disaster recovery. In this article, we dive a little deeper into AI and Deep Learning-based ML technologies, the benefits they offer, and how Octo helps customers build the right architecture to meet their needs.
What markets use AI/ML/DL?
Advanced AI, ML, and DL models are widely used in a variety of arenas, including health. They have progressed into all segments that require or have the desire to implement advancements in application data management and analysis. Efforts that were once performed by various operations, monolith application processes, or manually can now be handled by automated processes. The result is a compilation of data results and reports for business management, performance, projections, and security awareness.
What functions do AI/ML/DL offer in cloud services?
The trend in AI/ML/DL is for organizations to implement hyperautomation—automating anything that can be automated—through software and applications. As more data is introduced (genomic, behavioral, quantitative, imagery, etc.), AI and ML help data scientists look for patterns and predict how diseases may react to certain conditions or treatments. Rather than perform these tasks manually, scientists can use AI/ML to process large quantities of data and analyze behavioral patterns in much shorter periods of time. The benefits of AI/ML/DL also extend into cybersecurity across an enterprise, aiding in heightened security and analysis to combat and stop threats. Some examples of specific functions include:
- Forecasting/Predictive Analysis: Technologies trending in AI/ML include the internet of things (IoT) and forecasting and/or predictive analysis. Forecasting and predictive analysis involve analyzing historic data or specific data points to help determine a forecast for reporting. Predictive analysis relies on pattern recognition, modalities, and motifs for deeper analysis and advanced reporting.
- Tactical Data: AI in health can focus on analyzing patient data to identify health related issues. This arms doctors and scientists with information to diagnose conditions and determine treatment plans more accurately. AI can also help interpret device scan images and further medical research and advancements.
- Consumer Trends: AI and ML are no longer just used by scientists and advanced technical operations. Now, organizations that want to advance their ability to manage and capture data via hyper-automation use AI and ML to evaluate and defend against cybersecurity threats and attacks. AI delivers deeper and more accurate insight into attack attempts and anomalies to distinguish an attack from a consumer conducting business. AI and ML also help build forecast business models by capturing consumer trends through data analysis of specific motifs and behaviors.
- Data Processing: AI and ML technologies advance data processing through a collection of data types that range from images, video, audio, and text to HTML and others. AI data automation consists of collecting, qualifying, and organizing information and tracking consumer behaviors to help agencies make better decisions with greater confidence.
What infrastructure is needed to support AI/ML/DL?
- Serverless: Serverless allows agencies to operate and run code on demand related to a specific event. AI and ML can run on serverless computing that automatically provisions the compute resources that an agency requires without having to manage this effort. It can also scale as needed to handle the influx of data and loads that are introduced. Conversely, AI and ML can throttle down the use of resources when they are not required, thus controlling costs and operations.
- Containerization (Managed or Unmanaged): Containerization with AI/ML models is increasingly popular within the DevOps space because it allows DevOps to deploy via automation. It also offers agencies the ability to roll back deployments without interruption and helps minimize errors and bugs in builds and packages. Incorporating AI/ML into Continuous Integration and Continuous Deployment (CI/CD) pipelines provides numerous benefits, including more rapid deployments, lower risk, and greater insight in the code, along with efficient build and deployment processes of the agile development life cycle.
- Virtual (Managed or Unmanaged): Virtualization offers infrastructure to support AI/ML and DL applications respectively. These technologies are platform agnostic and based on the framework model.
- On-Premises or Cloud-Hosted (Cloud-Native or Hybrid): Whether an agency’s environment is on-premises, in the cloud, or a hybrid of the two, AI/ML/DL applications can operate across a variety of technologies. These application models can integrate into existing infrastructure and development models that operate within a microservices design and application stack that the agency already uses.
How does Octo help health customers implement AI/ML/DL technologies?
Octo has demonstrated experience with design and implementation of cloud services and adoption of cloud native or hybrid models with on-premises and cloud services managing IT infrastructure compute, data, and application services in seamless operations. This includes support of all health-related security, compliance, and regulatory requirements across the footprint. Cybersecurity measures are also at the forefront of our architecture design and development processes.
We leverage current advanced scaled Agile methodologies to help develop and adapt to the cloud services implementation and use of technologies. We train and work with leadership to adopt these Agile principles, allowing them to lead teams and build a mindset from the top down of the organization. This engages the agency in the technology workflow and development activities while removing the barriers of legacy or common processes to a new and resourceful practice.
Our experts introduce AI/ML/DL related technologies in the data capturing and analysis supporting data scientists in health-related developments and testing, as well as result analysis, to identify and learn about new medical procedures and medicines that can provide health and wellness benefits. These studies provide preventative actions and procedures to aid in health and to combat diseases. AI and ML technical advancements reduce time of analysis and provide more accurate results from automated processing large sets and series of data.
What sets Octo apart in helping clients navigate the cloud services realm?
Octo’s strength is in its people. Our highly skilled staff of subject matter experts include developers, engineers, and architects who are trained across all technology stacks to support any given project, process, implementation, or support mission. We strive to advance skills in new innovative technologies through proactive training and qualified certifications across various IT and industry standards, practices, and methodologies. We participate and support our customers’ environments through development strategies and coaching and leading Agile processes, supporting IT operations, all while providing innovative technology advancements, such as AI, ML, and DL related technologies.
Our Federal Government niche has provided us with the specific training and skills that align with security, compliance, methodologies, and regulatory requirements of agencies and organizations—including those in health—and the respective technologies and cloud services that best support them. In short, we know our customers from the inside out and the challenges they face. We offer efficiency in design because we already know their missions and priorities.