MLOps Practice

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

Robin Meehan, co-founder & MD, Inawisdom

Inawisdom MLOps Practice

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

To do this, we deploy a multi-skilled team that specialises in DataOps and MLOps techniques added to the more traditional DevOps approach.

What we deliver

The practice is responsible for architecting serverless solutions, building applications, automating data pipelines and integrating ML models with enterprise architectures. The projects the MLOps practice undertakes cover the full solution lifecycle: from prototype, using AWS ML services to full production deployments, using DevOps and following AWS best practices.

How we deliver

MLOps 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 MLOps in-build prototypes to showcase the future potential that ML could have as a differentiator or disruptor in their market or industry.

A CLOUD-NATIVE APPROACH

Inawisdom uses a cloud-native and serverless approach to the solutions our engineers create. This involves using Lambda, API Gateway and StepFunctions as critical elements of the architectures and micro-services that we develop. These services also provide the foundation for creating MLOps processes that support the operation of machine learning models at scale. Lastly, we pride ourselves on educating clients on the use of serverless technology including how to deploy, debug, optimise and observe the operation on Lambda function in production workloads.

Learn more about Inawisdom’s use of Lambda here.

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.

Learn about RAMP on AWS at the AWS Solutions Space.

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‘This is my Architecture’ with AWS

Machine Learning and Automated  Model Retraining with SageMaker

MLOps 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 MLOps 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 of the most critical aspects that our MLOps 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.