Operationalize machine learning with the Model Factory Framework

Businesses increasingly rely on data to make decisions, as they attempt to re-create successes — and avoid failures — of the past. Traditionally, this means businesses have taken a reactive approach, where they make decisions for tomorrow, based on performance data from the past.

But with machine learning, businesses can now harness their data to peek into possible future outcomes. From financial forecasting, churn prevention and predictive maintenance, to inventory management and simply identifying the next best action, machine learning is empowering businesses to make better-informed decisions.

While machine learning is an incredibly powerful tool, implementing machine learning models for real-world application can be highly challenging. In fact, according to IDC, over a fourth of AI and machine learning initiatives fail. The culprits are multi-faceted:

• Lack of developer experience with machine learning
• Poor data quality and challenging operationalization
• Time-consuming processes, such as the need to repeatedly train new datasets
• Lack of a standardized set of best practices that integrate CI/CD, DevOps, DataOps and software engineering practices
• An abundance of tooling, processes and frameworks — and data and operations teams that have their own, unique preferences

In order to address these challenges and bridge the gap between teams, you need a standardized framework, agnostic of platform or tooling.

The Model Factory Framework

The Rackspace Technology Model Factory Framework is designed with all of these challenges in mind. It provides a coherent mechanism, so that your organization’s data and operations teams can collaborate, develop models, automate packaging and deploy to multiple environments — while preventing deployment delays, incompatibilities and other problems.

It’s a cloud-based machine learning lifecycle management solution — an architectural pattern rather than a product. Also, since it’s open and modular, you can integrate it with AWS services and industry-standard automation tools such as Jenkins, Airflow, AWS CodePipeline for data processing.

And given that the machine learning lifecycle is complex, with multiple building, training, testing and validation stages — across data analysis, model development, deployment and monitoring — the Model Factory Framework integrates Amazon SageMaker, an AI and machine learning services stack that includes:

• AI services that provide pre-trained models for ready-made vision, speech, language processing, forecasting and recommendation engine capabilities
• Machine learning services that provide pre-configured environments within which you can build, train and deploy deep learning capabilities into your applications

The Amazon SageMaker stack also supports all the leading machine learning frameworks, interfaces and infrastructure options, for maximum flexibility.

Key benefits of the Model Factory Framework

The Model Factory Framework can help you cut the entire machine learning lifecycle from more than 25 steps, down to under 10. It further accelerates the process by automating handoffs between the different teams involved and by simplifying troubleshooting — which it achieves by supplying a single source of truth for machine learning management.

• For data scientists, the Model Factory Framework provides a standardized model development environment, the ability to track experiments, training runs and resulting data, automated model retraining and up to 60% savings on compute costs through scripted access to spot instance training and hyperparameter optimization (HPO) training jobs in QA.
   
• For operations teams, the framework automates model deployment across development, QA and production environments. It also provides a registry for model version history tracking as well as tools for diagnostics, performance monitoring and mitigating model drift.
   
• For the organization, the framework provides a model lineage for governance and regulatory compliance, improves time to insights and accelerates ROI, while reducing effort to get machine learning models into production.

Get started with the Model Factory Framework

If you would like to learn about the Rackspace Technology Model Factory Framework in more detail and explore how it improves processes — from model development to deployment, monitoring and governance — download our whitepaper, “Moving from machine learning models to actionable insights faster,” where we explore:

• An overview of the machine learning lifecycle and its challenges
• How DevOps practices are misaligned to the machine learning lifecycle
• The Model Factory Framework overview, tools and processes
• How the Model Factory Framework cuts model deployment from 25 to as few as 10 steps