Operationalize Models at Scale using Pre-Packaged MLOPs Pipelines

Background Digital transformation through AI/ML adoption can be challenging for organizations to put into production due to complex processes. Digital Alpha's solutions streamline management of model selection, deployment, and monitoring, enabling businesses to automate their ML pipelines on AWS and revitalize their offerings with ease.

Features

• Accelerate model development with repeatable workflows for quick iteration and improved productivity.

• Ensure reproducibility and governance with centralized ML artifacts for easier management and maintenance of the model lifecycle.

• Achieve faster time to production with automated ML workflows and CI/CD pipelines.

• Continuously monitor data and models in production for quality assurance and improved performance.

Benefits

• Increase productivity with self-service environments and curated data sets

• Ensure repeatability with automation of MLDC steps

• Achieve reliability with CI/CD practices for quick and consistent deployment

• Enable auditability with versioning of all inputs and outputs

• Ensure data and model quality with MLOps policies to guard against bias and track changes over time.

Common Use Cases

• Financial Monitoring: Monitor financial transactions and activities to detect potential fraudulent or suspicious behavior, and ensure compliance with regulations and policies.

• Investment Predictions: Use machine learning algorithms to analyze financial data and make predictions about investment opportunities, helping investors make informed decisions.

• Risk Management: Analyze and manage financial risk by leveraging machine learning to identify potential risks, quantify their impact, and develop strategies to mitigate them.

• Algorithmic Trading: Use machine learning models to analyze market data and make informed trades, helping traders to maximize profits and minimize risks.

• Process Automation: Automate financial processes and workflows using machine learning to increase efficiency, reduce errors, and improve overall performance.

Deliverables

• Discovery Workshop: Identify tools, ML models and cloud landscape to define the business problem and design, propose a solution

• Design and Roadmap: Propose detailed design of the solution based on the information collected from the discovery workshop

• Implementation: Provide a CI/CD pipeline for the models to implement the proposed solution

• Integration: Integrate the models in the applications to get to production

Five different options:

Quick and cost-effective approach to deploy pre built models

• Publish pre-trained inference models as an API using Lambda functions
• Deploy ML models for inference on AWS Lambda using API gateways, EFS and S3 buckets

Manage Machine Learning lifecycle with MFlow and Amazon Sagemaker

• Provides solution on how to deploy MLflow on AWS Fargate and use it for the ML project with Amazon SageMaker.
• SageMaker develops, trains, tunes and deploys ML model using the dataset. Track experiment runs and models with MLflow during ML workflow.

Amazon SageMaker built-in algorithm MLOPs pipeline using AWS CDK

• Provides a solution for MLOps Pipeline, where MLOps Pipeline includes data ETL, model re-training, model archiving, model serving and event triggering.
• Although this solution provides XGBoost as an example, it can be extended to other SageMaker built-in algorithms because it abstracts model training of SageMaker's built-in algorithms.
• Various AWS services(Amazon SageMaker, AWS Step Functions, AWS Lambda) are used to provide MLOps Pipeline, and those resources are modeled and deployed through AWS CDK.

Deploy and manage Machine Learning pipelines with Terraform using Amazon SageMaker

• One possible approach to manage AWS infrastructure and services with IaC is Terraform, which allows developers to organize their infrastructure in reusable code modules. This aspect is increasingly gaining importance in the area of machine learning (ML).
• Developing and managing ML pipelines, including training and inference with Terraform as IaC, allows customers scale for multiple ML use cases or Regions without having to develop the infrastructure from scratch.

MLOPS workload orchestrator

• The MLOps Workload Orchestrator solution helps streamline and enforce architecture best practices for machine learning (ML) model productionization. This solution is an extendable framework that provides a standard interface for managing ML pipelines for AWS ML services and third-party services.
• The solution’s template allows customers to train models, upload their trained models (also referred to as bring your own model), configure the orchestration of the pipeline, and monitor the pipeline's operations. This solution increases team’s agility and efficiency by allowing them to repeat successful processes at scale.