Enterprises trying to leverage Artificial Intelligence (AI) and Machine Learning (ML) technologies face significant hurdles. Surveys indicate almost 60% to 85% of AI/ML models never reach deployment. The root cause points to a need for multidisciplinary collaboration, data quality improvements, and preparing for post-production needs and challenges. AI Assembly, an enterprise AI/ML adoption framework addresses these obstacles and streamlines the path to AI/ML adoption.
AI-First Enterprise Enablement
Overview
AI Assembly consists of three distinct phases –
Invest, Develop, Harvest.
Its structured methodology emphasizes collaboration, innovation, and practical implementation, guiding enterprises from conceptual exploration to successful operational integration.
Collaboration and Exploration
Starts with the importance of cross-functional collaboration among business users, data scientists, and IT teams to identify viable use cases for AI/ML projects through discovery workshops.
Test and Validate
Pilot projects using actual available data and proven models to forecast potential success and understand implementation nuances in production.
MLOps Consulting and Strategy
Explore ML capabilities and limitations, and develop a tailored MLOps strategy, key to success in production.
Explainability Evaluation
Evaluate explainability and interpretability needs to match stakeholder expectations for compliance, trust, and transparency.
Risk Assessment
Understand the risks and consequences of AI-driven decisions and plan for moderation, controls, and governance needs.
Data Collection
Involves gathering essential data from diverse sources in coordination with IT and data teams. This includes data from internal databases, public datasets, or APIs ensuring the data’s relevance and sufficiency for in-depth analysis.
Data Cleaning and Preparation
This step involves preprocessing of data by handling missing values, removing outliers, and converting data into a format suitable for analysis. Clean data is essential for accurate models and analysis.
Exploratory Data Analysis
Analyze data and summarize its main characteristics. This step helps uncover patterns, spot anomalies, test hypotheses, and check assumptions through descriptive statistics and visualizations. This lays the groundwork for informed decision-making.
Feature Engineering
Involves transforming raw data into features that accurately represent the problem at hand, enhancing the predictive models’ performance. This phase may include creating new features or selecting and encoding relevant variables.
Modeling and Evaluation
Involves selecting and training machine learning models tailored to the data’s features and complexity, with a focus on explainability, and tuning for peak performance. Effectiveness is measured on a separate validation dataset, possibly requiring model refinement, fine tuning, or alternative approaches for enhanced outcomes.
Deployment
Integrate the model into the existing production environment and user workflow where it can make predictions with new data. Deployment also includes helping the users and stakeholders apply the insights in decision making and explain the results.
Monitoring and Maintenance
A well thought out MLOps process ensures high model accuracy and relevance. End-to-end MLOps platform implementation for seamless IT integration, workflow automation and monitoring of performance drift, other post production activities.
Governance and Compliance
A governance and control process to ensure explainability, regulatory compliance, risk mitigation and management to ensure trust and confidence in the models.
Training and Enablement
Train stakeholders on MLOps and best practices with managed services for ongoing support of all activities post production.
Communication and Education
Stress the importance of clear communication regarding model capabilities and limitations, and its use in decision-making.
AI Assembly Advantages
Accelerated Time to Market
Speed up from several months to weeks
Lower Risk of Failure
Test and validate use cases with stakeholders
Cost Savings
Smart planning and resource efficiency for maximum ROI
Trust and adoption
Comprehensive data management with XAI for accuracy
Competitive Advantage
Continuous Optimization for dynamic business needs
Success Stories
Case Studies