ML Solution Architect - Introduction

A Machine Learning (ML) Solutions Architect designs, develops, and manages machine learning (ML) systems and models to help organizations solve problems and innovate.?

ML Solutions Architects focus on identifying and applying ML algorithms to address problems like predictive analytics, computer vision, or natural language processing. They don’t develop new machine algorithms, which is better suited for applied or research data scientists. Instead, their goal is to validate approaches for further experimentation by full-time data scientists.

ML solutions architecture divisions and coverage:

• Business understanding: Business problem understanding and transformation using AI and ML.
• Identification and verification of ML techniques: Identification and verification of ML techniques for solving specific ML problems.
• System architecture of the ML technology platform: System architecture design and implementation of the ML technology platforms.
• MLOps: ML platform automation technical design.
• Security and compliance: Security, compliance, and audit considerations for the ML platform and ML models.

Business understanding and ML transformation

The goal of the business workflow analysis is to identify inefficiencies in the workflows and determine if ML can be?applied to help eliminate pain points, improve efficiency, or even create new revenue opportunities.

Identification and verification of ML techniques

Once you have come up with a list of ML options, the next step is to determine if the assumption behind the ML?approach is valid. This could involve?conducting a simple?proof of concept?(POC) modeling to validate the available dataset and modeling approach, or technology POC using pre-built AI services, or testing of ML frameworks. For example, you might want to test the feasibility of text transcription from audio files using an existing text transcription service or build a customer propensity model for a new product conversion from a marketing campaign.

It is worth noting that ML solutions architecture does not focus on developing new machine algorithms, a job best suited for applied data scientists or research data scientists. Instead, ML solutions architecture focuses on identifying and applying ML algorithms to address a range of ML problems such as predictive analytics, computer vision, or natural language processing. Also, the goal of any modeling task?here is not to build production-quality models but rather to validate the approach for further experimentations by full-time applied data scientists.

System architecture design and implementation

The most important aspect of the ML solutions architect’s role is the technical architecture design of the ML platform. The platform will need to provide the technical capability to support the?different phases of the ML cycle and personas, such as data scientists and operations engineers. Specifically, an ML platform needs to have the following core functions:

• Data explorations and experimentation: Data scientists use ML platforms for data exploration, experimentation, model building, and model evaluation. ML platforms need to provide capabilities such as data science development tools for model authoring and experimentation, data wrangling tools for data exploration and wrangling, source code control for code management, and a package repository for library package management.
• Data management and large-scale data processing: Data scientists or data engineers will need the technical capability to ingest, store, access, and process large amounts of data for cleansing, transformation, and feature engineering.
• Model training infrastructure management: ML platforms will need to provide model training infrastructure for different modeling training using different types of computing resources, storage, and networking configurations. It also needs to support different types of ML libraries or frameworks, such as?scikit-learn,?TensorFlow, and?PyTorch.
• Model hosting/serving: ML platforms will need to provide the technical capability to host and serve the model for prediction generations, for real-time, batch, or both.
• Model management: Trained ML models will need to be managed and tracked for easy access and lookup, with relevant metadata.
• Feature management: Common and reusable features will need to be managed and served for?model training and model serving purposes.

ML platform workflow automation

A key aspect of ML platform?design is?workflow automation?and?continuous integration/continuous deployment?(CI/CD), also known as MLOps. ML is?a multi-step workflow – it needs to be?automated, which includes data processing, model training, model validation, and model hosting. Infrastructure?provisioning automation and self-service is another aspect of automation design. Key components of workflow automation include the following:

• Pipeline design and management: The ability to create different automation pipelines for various tasks, such as model training and model hosting.
• Pipeline execution and monitoring: The ability to run different pipelines and monitor the pipeline execution status for the entire pipeline and each of the steps in the ML cycle such as data processing and model training.
• Model monitoring configuration: The ability to monitor the model in production for various metrics, such as data drift (where the distribution of data used in production deviates from the distribution of data used for model training), model drift (where the performance of the model degrades in the production compared with training results), and bias detection (the ML model replicating or amplifying bias towards certain individuals).

Security and compliance

Another important?aspect of ML solutions architecture is the security and compliance consideration in a sensitive or enterprise setting:

• Authentication and authorization: The ML platform needs to provide authentication and authorization mechanisms to manage access to the platform and different resources and services.
• Network security: The ML platform needs to be configured for different network security controls such as a firewall and an IP address access allowlist to prevent unauthorized access.
• Data encryption: For security-sensitive organizations, data encryption is another important aspect of the design consideration for the ML platform.
• Audit and compliance: Audit and compliance staff need the information to help them understand how decisions are made by the predictive models if required, the lineage of a model from data to model artifacts, and any bias exhibited in the data and model. The ML platform will need to provide model explainability, bias detection, and model traceability across the various datastore and service components, among other capabilities.

Various industry technology?providers have established best practices to guide the design and implementation of ML infrastructure, which is part of the ML solutions architect’s practices. Amazon Web Services, for example, created?Machine Learning Lens?to provide architectural best practices across crucial domains like operational excellence, security, reliability, performance, cost optimization, and sustainability. Following these published guidelines can help practitioners implement robust and effective ML solutions.

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