DevOps Practice

“Automation of the deployment and management of infrastructure, application components and software releases is central to Inawisdom’s ability to deliver business impact – and not just once, but repeatedly, reliably and rapidly.”

Neil Miles, CEO, Inawisdom

Inawisdom DevOps Practice

The Inawisdom DevOps practice is a core capability, responsible for taking Machine Learning (ML) and data driven projects and embedding  them in businesses processes systems to deliver value. Once embedded, we maintain these to ensure the value continues long term and drives on-going business value.

What we deliver

The practice is responsible for architecting serverless solutions, building applications and integration with enterprise architectures. The projects the DevOps practice undertakes cover the full solution life-cycle: from prototype, using AWS ML services to full production deployments, using DevOps and following AWS best practices.

How we deliver

DevOps is essential to the full life cycle capability that we offer to our customers; it is a process and culture that allows us to quickly deliver value by taking ML and delivering it into the heart of a customer’s business. In addition, we also use DevOps in-build prototypes to showcase the future potential that ML could have as a differentiator or disruptor in their market or industry

Inawisdom was founded to give our customers the ability to exploit every aspect of their data using Artificial Intelligence on AWS. Inawisdom are leaders in AI and Machine Learning, Advanced Analytics and Data Science, providing a “full stack” of AWS Cloud and Data Services.

Our Solution Offering 

Benefit from Inawisdom’s expertise and proven delivery

Inawisdom’s Rapid Analytics and Machine Learning Platform (RAMP) solution helps  organizations quickly ingest and analyse vast amounts of data from different sources to gain significant advantages from machine learning and AI services. Find out more about our platform

Learn about RAMP on AWS at the AWS Solutions Space

DevOps drives success and value from various use cases

Rapid Proof of Value

Building ML base solutions taking data science outputs e.g. Jupyter notebooks, trained model and AWS ML Services, and through DevOps practices quickly enable business validation of the project hypothesis and showcasing the capabilities of ML.

Productionisation

Productionising ML solutions, ensuring they are highly secure, reliable, performant and cost efficient.

The deployment of ML models and associated components using build tools and Infrastructure as Code to provide consistency and traceability.

Operationalisation

The ongoing automated monitoring and alerting is built into solutions to ensure high availability and that target response times are met.

Providing KPI’s to judge business impact and service levels are understood, measured and achieved

Uniquely integrating tools such as AWS X-Ray to provide complete observability of ML in production to identify issues, bottlenecks or potential optimisations.

Data Science Model Retraining

One the most critical aspects that our DevOps practice leads on is retraining of models and using multiple deployment options (depending on the customer need) when updating them.

Use of Continuous Integration and Continuous Deployment pipelines that take new data, cleans and transforms the data, runs incremental training of ML models, verifies their accuracy and then deploys them into production.