Build Lab – MLOps

Machine learning expands the intelligent capabilities of your organization. Customer feedback and sentiment analysis provides a reflection of the impact you have on your customers, enabling you to better address their needs and concerns, while fraud detection and predictive maintenance processes help to protect your customers and hardware investments.

Data scientists focus on model creation, leveraging notebooks to train and tune the models which provide the functionality you require, but they typically do not have the background to create pipelines to support model build and deployment, feature engineering, iterative training, and model monitoring.

AWS services provide a rich suite of functionality to handle all aspects of your ML pipeline. The Uturn MLOps Build Lab steps your data engineers through the creation and deployment of your ML pipelines, delivering your real world use cases as the output of the engagement.

Uturn’s MLOps Build Lab is a cooperative engagement spanning 4-6 weeks which culminates in a one-week build activity guided by a certified Senior Architect. The build team consists of your developers or Uturn engineers, or a combination of both, depending upon your needs. Uturn believes collaboration is the best approach because it includes your builders from the beginning reducing future knowledge gaps.

Throughout the engagement, you will have an assigned Technical Project Manager and Senior Architect. Through a series of planning meetings Uturn will step through your use cases, assess your current state, propose an architecture, and ensure that the team is ready to build.

The result of build week is a pilot implementation of your application, built using AWS services alongside tools or SaaS offerings that are already in your arsenal.

MLOps Build Lab Deliverables

When the lab is complete, you will have a pilot version of your application with the following attributes:

• Live Environment: Built and running in your own AWS environment
• Proven Use Cases: Deliverables are focused on connecting components and proving out critical functionality
• Actionable Playbook: Work is not “throw away”. Uturn will leave you with a plan to iterate the application all the way to production future use cases.

Examples of In-Scope AWS Services

Example Use Cases

• Personalized customer experience
• Batch sentiment analysis of customer feedback
• Real-time inference API creation
• Bringing Jupyter notebooks to production
• Computer vision for logistics operations such as box counting and object identification
• Real-time content moderation
• IoT anomaly detection, defect detection, and protective monitoring
• Prioritize and route customer inquiries and support requests in call center operations