Failing to create value with ML
Gaining a strategic advantage or optimizing business operations with ML is still rare and mostly limited to experimentation. It’s very challenging for organizations to make the transition from experimentations to actual deployments of ML applications. Several reports (e.g. Gartner articles) communicated a staggering 80% of all ML projects failing to deploy.
We clearly see a growing need for guidelines and best practices to apply while executing end-to-end ML projects, so that delivery on the promise of Machine Learning is finally enabled.
Creating business value with ML-driven applications will happen barely if not at all by not countering underlying challenges. Based on our ML experiences we’ve identified these key challenges:
ML Ops as an enabler
ML Ops draws on DevOps principles and practices. It consists of best practices for the delivery ML application and will enable you to address aforementioned types of challenges. Examples are:
Benefits of applying ML Ops
Applying ML Ops in your ML application delivery results in tangible benefits for your organization.
Our ML Ops principles
VQD brings speed and agility in delivering ML pipelines which automate processes across the ML lifecycle to build, train, deploy and monitor ML models. In order to do so, we consistently follow our ML Ops principles.
- INFRA AS CODE - We spin-up environments quickly with your preferred tools to build ML Models which best fit the business problems at hand
- MODEL TRAINING - We build scalable training pipelines which are able to handle advanced ML algorithms with big data
- APPLICATION DEPLOYMENT - We deploy flexible and scalable ML applications with an end-to-end monitoring of model accuracy and pipeline performance
- EFFICIENT WAY-OF-WORKING - We enable efficient and collaborative processes with our best practices for code, model and project repositories
- TECHNOLOGY AGNOSTIC - We build, train and deploy models either on premise, in the cloud (e.g. Azure, AWS, GCP) or in a hybrid model
- TRANSPARENCY BY DESIGN - We ensure that our end-to-end pipelines are explainable and auditable in order to enable regulatory compliance
VIQTOR DAVIS accelerates ML deployments
Our experience with ML Ops and developing ML pipelines to deploy applications enabled us to build off-the-shelf ML pipeline blueprints for different cloud platforms (e.g. MS Azure). An illustrative and conceptual example of such a ML pipeline is shown in the figure below.
On a high-level, an end-to-end ML pipeline consists of the following components:
- Build/CI pipeline component in which infrastructure is deployed as code; a pipeline deploys resources based on a template and the Train/Release pipeline components are orchestrated
- Train/CT pipeline component in which data is extracted from data storage and transformed, features are created. Trained models are evaluated and validated, after which they are stored in a model registry
- Release/CD pipeline component in which models from the registry are packaged and deployed to be used for the serving of new predictions. Performance results, data drifts & faults are monitored and logged
By applying our ML Ops principles and readily available ML pipelines, we have enabled many organizations to generate significant business value through Machine Learning. Below an example of a project where VQD leveraged its ML Ops capabilities for a partner in the financial industry.
VIQTOR DAVIS offers a unique combination: We are your partner in data strategy, governance, management, science and analytics. We provide professional services, full-service solutions and knowledge transfer. We call this Data Craftsmanship. Interested in finally generating the promised business value from your ML initiatives? Get in touch with our experts.