Achieve AI at scale with ModelOps (MLOps)

Candice Gouws

The consensus is growing that model operationalization, rather than model development, is today's most significant hurdle for data science.

The ModelOps decision process
The decision process - Image Reference SAS

The consensus is growing that model operationalisation, rather than model development, is today's most significant hurdle for data science.

Production deployment techniques are generally one-offs. Application integration, model monitoring and tuning, and the flow of work are often afterthoughts. Often called the "last mile" for analytics, this is where data science meets production and where organisational value is or (is not) created.

Achieving the goal of leading a model-driven business that deploys and iterates models at scale requires something that only a few companies have: ModelOps.

"Only about 50% of models are ever put into production." - Reference SAS

Introduction to ModelOps

ModelOps is a holistic approach for quickly and iteratively progressing models through the analytics life cycle. ModelOps focuses on the application development community's DevOps approach, where DevOps focuses on application development, ModelOps focuses on getting models from the Lab, validation, testing, and deployment as quickly as possible while ensuring quality results.

ModelOps, which encompasses culture, processes, and technology, enables you to seamlessly, efficiently, and continuously develop and deploy models so you can cross the last mile and ensure analytics delivers on its promise. ModelOps is how models are cycled from the data science team to the production team in frequent, regular deployments.

In the race to gain value from AI models, it's a critical methodology that only a handful of organisations are using. More and more, businesses rely on machine learning (ML) models to turn massive volumes of data into fresh insights and information, vast amounts of unstructured data used to identify patterns for predictive purposes.

'ModelOps industrialises the use of models in your organisation so you can reap the full benefit of AI'

With ModelOps, enterprises can:

  • Deploy new or updated models quicker
  • Monitor model performance across your platforms
  • Govern models and ensure compliance with regulatory and business risk requirements
  • Automate ModelOps processes to drive lean, agile, and best engineering practices
  • As part of your ModelOps efforts, it's crucial to accurately assess the data sources and variables available for use by your models so that you can answer:
  • What data sources will you be using?
  • Are you comfortable telling your customers that a decision was made based on this data used?
  • Are you addressing model bias?
  • How frequently are data fields edited?
  • Are you able to replicate feature engineering in your production environment?

However, model development and deployments are complicated, and only about 50% of models are ever put into production. On top of that, models that are, take at least three months to be ready for deployment. This time and effort equate to high operational costs and a slower time to value. All models degrade, and if they aren't given regular attention, performance suffers. Analytical Model performance considers multiple variables, model construction, data, fine-tuning, frequent updates, and retraining.

There are a few common problems addressed using a ModelOps approach. Models can degrade as soon as deployed, and some things will affect your models' performance more than others. Below are some common problems that you will almost certainly encounter.

Time to deploy

Model development and deployment can be a lengthy process; firstly, you would need to determine how long that cycle is within your organisation, set benchmarks to measure improvements. Next, you would need to break the process down into steps, then measure and compare projects to identify best / worst practices. A Model Management solution could also be an option to automate some activities.


Degradation

Be aware of model bias. To answer these problems is creating a robust approach to model stewardship in your organisation. If everyone takes ownership of your models' integrity, these problems will resolve before they affect your bottom line.

The danger of organisations ignoring ModelOps

The power of making use of predictive models, in conjunction with the availability of big data and an increase in computational power, will continue to be a source of competitive advantage for smart organisations. Those who fail to embrace ModelOps face growing challenges in scaling their analytics and fall short of the competition.

"We provide an end to end solution that gives you the peace of mind knowing that a trusted technology partner is meeting your objectives."

So how can EBlocks Software help you?

We realise the challenges of producing models in the enterprise environment. We, therefore, build cross-functional, multidisciplinary teams. We create high quality, scalable solutions to solve your toughest challenges. 

Our approach combines agile, lean, and continuous delivery methodologies to deliver smart solutions; we build quality into the delivery process. Our partnerships with AWS, DataRobot, and the Wits Institute of Data Science, help us serve you holistically as a customer. 

We provide an end to end solution that gives you the peace of mind knowing that a trusted technology partner meets your objectives.

Reach out to us to find out more by visiting our website www.eblocks.co.za or send us a mail at sales@eblocks.co.za

Latest INSIGHTS

Eblocks Helps Major Bank deliver and Enterprise Metadata Solution

Read this insight

Data Culture in your cloud journey

Read this insight

Why our hiring process looks beyond the resume

Read this insight