RACI of an AI Project
Responsible, Accountable, Consulted, and Informed
In enterprise AI endeavors, an intricately defined RACI framework is crucial. It clarifies roles during phases like Data and Features, where DE ensures data integration and ETL, while FE crafts feature and collaborates with Data Science. In the Prepare phase, DS aligns goals and FE readies data. During Build, DS, MLE, FE shape models, and IT/DevOps create infrastructure. Ready to Release involves MLE, MLOps, FE, and IT optimizing models. API/WEB and BI reporting feature IT's app construction and BID's dashboard crafting. The RACI framework's role is pivotal—structuring responsibilities, promoting collaboration, and ensuring efficient data use, model optimization, and successful releases. It drives transformative outcomes by fostering transparency, alignment, and effectiveness across diverse teams.
Data and Features
Phase Data and Features - DE/FE
Within the context of an enterprise's Artificial Intelligence initiative, roles harmonize within a clearly defined RACI framework. Data Engineers (DE) carry the mantle of accountability and responsibility in ushering data from diverse sources into GCS, while activating the Gold and Silver layers. They retain ownership over building pertinent ETL and data pipelines.
FE teams adeptly harness Gold and Silver layer data to curate feature tables within CDL (curated data layer). Expanding further, they craft feature-enriched entities within the feature store, priming them for data science model deployment. FE directly collaborates with Data Science, deciphering upcoming feature requisites and liaising with Data Engineering to integrate data from third-party sources, like SAP.
Promoting seamless collaboration, FE strives to provide ample advanced notice to the DE team, allowing sufficient time for timely deliveries. While exact timeframes are under discussion, a FE product manager spearheads coordinated timelines among all involved teams. This orchestrated collaboration among DE, FE, and relevant entities underscores the enterprise's commitment to efficient and impactful AI projects.
Model in Dev and PreProd
Phase Prepare - DS/FE
In the initial phase of project preparation, the collaboration among diverse roles within the AI ecosystem is pivotal to ensure a structured and well-coordinated project progression.
Data Scientists (DS) play a crucial role in articulating the business requirements, aligning the project scope with organizational goals, and outlining the strategic planning for implementation, along with identifying key performance indicators (KPIs) that will gauge project success. Simultaneously, DS spearheads the Proof of Concept (POC) development, laying the foundation for subsequent phases.
Feature Engineers (FE) actively contribute by ensuring that the necessary data and features are primed for POC experimentation. FE also establishes a robust data and feature abstraction mechanism, which acts as a pivotal link in the subsequent phases.
This early collaboration, with DS driving requirements alignment and POC creation, and FE enabling data readiness and abstraction, sets a strong foundation for the upcoming stages of the AI project, fostering efficiency, transparency, and seamless progress.
Phase Build - DS/MLE/FE
In the developmental phase of the Artificial Intelligence project, a seamless collaboration among various specialized roles becomes pivotal to its successful realization within an enterprise context.
In this crucial phase, Data Scientists (DS), Machine Learning Engineers (MLE), and Feature Engineers (FE) come together in a synergistic alliance. DS takes charge of shaping the business solution and meticulously documenting the intricate aspects. They pioneered the creation of a Proof of Concept (POC) with distinct demarcations between Data/Feature and Model components. Ensuring code readability, optimizing complexity, and comprehensive documentation remains paramount.
In a concerted effort, DS, MLE, and FE jointly deliberate and make judicious model technology choices, craft an architectural blueprint, and chart an astute model deployment roadmap. Industrialization of model code through modularization, fine-tuning, optimization, and rigorous code reviews are also orchestrated by the DS and MLE. The cross-functional engagement extends to MLE, MLOps, and DevOps engineers who collaboratively establish the model's infrastructure, incorporating platform provisioning, repository management, and operational tools.
Simultaneously, FE, DS, and MLE synergize their expertise in selecting feature technologies, designing architectural blueprints, and plotting deployment strategies. Harmoniously, DS and FE guide the modularization of data/feature code, while FE collaborates with DevOps to actualize feature infrastructure within the designated platform, fortified by essential tools and repositories.
Lastly, the imperative of SQL and Spark optimization underscores the collaborative role of FE, MLE, and DS, wherein collective efforts are directed to ensure efficiency and high performance. This orchestration of roles and responsibilities in the developmental phase of an AI project underscores the enterprise's commitment to excellence and innovation, paving the way for a transformative journey in artificial intelligence.
Phase Ready to Release - MLE/MLOPS/FE
In the critical phase of preparing for release in an Artificial Intelligence project, the convergence of specialized roles orchestrates a seamless path to deployment within the enterprise landscape. Machine Learning Engineers (MLE) and Machine Learning Operations Engineers (MLOps) take the helm in optimizing the model for production readiness, ensuring its integration mechanism, and pioneering model monitoring and serving protocols. Their collaborative expertise forms the bedrock for successful model deployment.
Concurrently, Full Stack Engineers (FUE) and IT Engineers collaboratively embark on building and monitoring APIs that bridge the technology spectrum. Their synergy extends to constructing and overseeing both the front-end and back-end web solutions. Feature Engineers (FE) contribute decisively by streamlining data ETL and feature generation through an efficient transition into the Feature Store or centralized Data Lake via Airflow.
In this cohesive endeavor, the orchestrated efforts of MLE, MLOps, FUE, IT, and FE fortify the project's stride toward release, exemplifying the enterprise's dedication to seamless deployment, enhanced efficiency, and meticulous quality assurance.
Phase API, WEB, and BI reporting - IT
In the dynamic landscape of an enterprise's Artificial Intelligence project, roles and responsibilities converge to ensure the smooth progress of critical phases. In the API/WEB phase, a seamless collaboration unfolds between Front-end Engineers (FEE), Full Stack Engineers (FUE), and Back-end Engineers (BEE). This collective force diligently constructs, tests, and prepares web and mobile applications, poised for release in the Production environment. These applications are geared to empower business stakeholders with the consumption of predictions, optimizing strategic decision-making.
Transitioning to the BI phase, the dedicated expertise of BI Developers (BID) comes to the forefront. Within this context, they methodically design, test and ready BI dashboards for reporting, all primed for deployment in the Production environment. This orchestrated collaboration across diverse roles showcases the enterprise's commitment to delivering AI-driven solutions that bridge the gap between innovation and tangible business value.
Model in Prod
Phase Released in Prod - MLE/MLOPS/IT
In the intricate fabric of an enterprise's Artificial Intelligence venture, role synergies manifest within a defined RACI structure. Machine Learning Engineers (MLE) collaboratively interface with Machine Learning Operations Engineers (MLOps), spearheading the release of Data Science (DS) models into the production environment. This orchestrated partnership ensures that business stakeholders gain access to high-quality predictions, facilitated by the toolings or BI reporting dashboards meticulously crafted by IT Engineers. This harmonious collaboration signifies the enterprise's commitment to delivering transformative AI solutions that resonate seamlessly across organizational landscapes.
