The Key Benefits of AWS Managed Services for Cloud Workloads
The growing adoption of AWS managed services in the enterprise provides benefits that go beyond reducing operational IT costs. It’s making businesses leaner and more agile, ultimately improving data-driven decision making. How?
It’s all about the accessories. AWS, of course, is best known for its cloud-based server (compute) and storage services. Many IT professionals know that these services alone provide a level of flexibility and scalability that an on-premise data center could never match.
But AWS also provides data analytics, data visualization, and data management tools that are designed to take advantage of the AWS cloud architecture.
In this article, we discuss some of these AWS tools and how ERPA is using them to help businesses realize faster and better decision making.
AWS Managed Services: The Power of the Data-Driven Enterprise
How do all these AWS tools drive better decision making? From our experience, ERPA has learned that our clients realize the following benefits, among others:
- Faster implementation of machine learning (ML)-enabled applications without the need for in-house ML development expertise;
- Reduced operational costs for machine learning (ML) model training and execution;
- Faster turnaround for new reports, dashboards, and other decision-making artifacts;
- More accurate insights;
And more.Among the many services in the AWS portfolio, several are aimed at helping businesses make better tactical and strategic decisions through improved data analytics.
Proactive, Preventative, and Optimized Capabilities
Taken together, the benefits of using AWS hosted managed services from partners like ERPA mean that businesses can reach better decisions faster by leveraging the storage and compute resources, data management services, and analytical tools made available in the AWS ecosystem.
All at a surprisingly low cost compared to implementing a similar environment in an on-premise data center, let’s have a look at a few of them:
AWS Artificial Intelligence and Machine Learning
A well designed, trained, and tested machine learning (ML) algorithm can uncover hidden patterns in data, identify important trends, and help predict both opportunities and risks.
Developing and training a machine learning (ML) algorithm typically requires robust compute resources, and AWS has as much as you would ever need.
In addition, businesses can leverage other machine learning (ML)-related AWS tools, such as:
Amazon Elastic Inference
Amazon Elastic Inference uses lower-cost compute resources to do the heavy lifting when analyzing your data to make inferences and predictions. This approach can reduce your operational costs for ML applications by as much as 75%.
Amazon Augmented AI
As part of the AWS Machine Learning suite, Amazon Augmented AI (A2I) facilitates human review of ML inferences to improve accuracy–an important component of any AI application implementation.
Amazon Comprehend
Amazon Comprehend is a natural-language processing (NLP) service that can analyze unstructured text data, such as service ticket notes, online reviews, and social media posts to extract sentiment trends and identify actions, products, services, and marketing that elicit the most positive responses.
The Amazon Forecast
Amazon Forecast is a time-series forecasting tool based on machine learning (ML) that automates much of the development work by analyzing your own historical data to identify the key attributes to model.
AWS Data Management
Getting the most out of your data requires a robust data-management environment. AWS provides a number of database products to help with the low-level database management, including:
- Amazon RDS, which provides access to several relational database platforms, including Oracle, SQL Server, MySQL, and others;
- Amazon DynamoDB, a managed “NoSQL” database;
- Amazon Aurora, a high-performance managed relational database;
- Amazon DocumentDB, which is designed for storage, retrieval, and management of documents.
AWS also provides tools for data warehousing (Amazon Redshift) and data lake management (AWS Lake Formation).
These higher-level data management tools make it easier to develop the queries behind robust data analytics, which ERPA’s team of AWS experts can implement with ease.
AWS Data Analytics
Data analytics, of course, is the art and science of identifying, extracting, and summarizing data and presenting it in order to answer important business questions.
In addition to the Artificial Intelligence (AI)-related tools mentioned above, AWS data analytics services include:
- Search and query tools, such as Amazon CloudSearch and Amazon OpenSearch Service;
- Amazon EMR for big-data processing;
- Interactive query tools, such as Amazon Athena for static data and Amazon Kinesis for video and audio streams;
- Amazon Quicksight for data visualization and dashboards;
- And more.
ERPA + AWS Hosted Managed Services for Cloud Operational Excellence
As experts in AWS Managed Services, ERPA is well positioned to help our clients get the most out of their AWS investment.
In particular, our AWS consultants can help design a cloud-based environment that is optimized for critical business decision making.
With ERPA taking care of your AWS environment, we not only provide the best mix of AWS services for your particular situation, we help keep a lid on costs to maximize your return on investment.
ERPA removes the burden of keeping track of the AWS services you use, thereby ensuring you pay for only those resources you need. Furthermore, you don’t need any in-house AWS expertise.
Let our experts design a cost-effective, cloud-based solution that meets all your decision-making needs and provides the competitive advantage you need to get ahead.If your organization’s decision making is hampered by poor data organization, underpowered compute resources, and lackluster analytical tools, visit our dedicated ERPA contact page and submit a request for more information or call us directly at (614) 718-9222.
