Category Whitepapers and Guides
Harry Miller, Head of Data & Analytics Practice
Artificial intelligence (AI) has been hailed as the new electricity due to its potential to transform business. Unfortunately, unleashing the full potential of AI is a challenge for many enterprises.
It’s proving to be a struggle to get the machine learning (ML) models underpinning new AI applications over the line. Despite widespread ML investment by businesses that understand the potential of this technology, a staggering 60% of ML projects initiated never make it into production. To put this into perspective, imagine a car manufacturer that has a budget to build 100 cars on its production line but only completes 40 cars. This is obviously an undesirable outcome and an unsustainable business model.
Once developed, problems persist. Gartner predicts: “through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organisation.”
One issue is that the growth and evolution of data science tools and technologies and expertise has not been supported with equally mature ML development methods or processes. This has a direct impact on the ability to effectively manage release cycles to deploy ML models in production and manage the continuous retraining and release cycles required. The remains a major barrier to the success of ML projects for most organisations.
If this sounds alarmingly familiar, don’t despair.
The emergence of MLOps – a practice for collaboration and communication between data scientists and operations teams to help manage ML development lifecycles – is a game changer.
At ECS, our MLOps service blends our deep expertise in enterprise-grade DevOps with our pedigree in IT operations service and support and ML engineering capabilities. Importantly, we help you clear not just the technical hurdles but the cultural ones too.
Our MLOps frameworks are built on Amazon SageMaker to simplify and automate ML workflows and get models into production faster and more cost effectively.
Interestingly, several enhancements were announced this month at AWS re:Invent that will bring even more workflow efficiencies. One is Amazon SageMaker Studio, an integrated development environment (IDE) that allows developers and data scientists to build, code, develop, train and tune machine learning workflows using a single interface.
Another announcement that caught my eye is Amazon SageMaker Model Monitor. It detects deviations such as data drift which degrade model performance over time, so you can take remedial action.
Amazon Sagemaker Operations for Kubernetes is yet another new and exciting capability. It enables organisations to release workflows with much-needed portability and standardisation, supporting security, high availability and regulatory requirements.
Around our MLOps frameworks we wrap our unique, tried and tested pod-style approach to enablement. Our MLOps Enablement Pods provide outcome-focused teams of data scientists and MLOps engineers that remain flexible in their resource profiling so that your day-to-day team is exactly what you need at every stage.
Specifically, our MLOps Enablement Pods operate in sprints, embedding in your teams for short periods to instil new skills and tooling as well as new processes and ways of working into your business, while keeping a tight control on costs. By keeping your pipelines clear, we help you get AI projects into production quickly and cost effectively.
To find out how our MLOps wizardry can help you extract more value from your ML strategy and unblock your AI project pipelines, please get in touch with our team here.