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.

• Example

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.