Posted February 28, 2023

How To Elevate Traditional AI/ML Development Life Cycles Into Trusted AI/ML Development Life Cycles

How To Elevate Traditional AI/ML Development Life Cycles Into Trusted AI/ML Development Life Cycles

Artificial intelligence and machine learning (AI/ML) follows a cyclical process to help companies acquire practical business value. The traditional AI/ML cycle has five steps to ensure that businesses can leverage the technologies and efficiencies introduced by AI.

The Traditional AI/ML Cycle

The AI/ML cycle begins with defining the project objectives. Business leaders must identify a problem and subsequently find opportunities to substantially improve operations, increase customer satisfaction, or create value. Business leaders must seek out subject matter expertise to help define their unit of analysis and prediction target, prioritize their modeling criteria, consider risks and success criteria, and decide whether to continue pursuing AI/ML applications or not.

The second step in the AI/ML cycle is to acquire and explore data. Business leaders must collect and prepare all the necessary data for machine learning. This step includes conducting exploratory data analysis, finding and removing target leakage, and feature engineering.

The third step in the AI/ML cycle is to model data. Modeling data starts with determining a target variable that business leaders want to understand better. This ensures that the AI/ML application can gain insights from the initially collected data. With this, businesses will run ML algorithms to select variables, build candidate models, and validate and select an appropriate AI/ML model.

The fourth step in the AI/ML cycle is to interpret and communicate model insights. This is a challenge in machine learning projects since it entails explaining model outcomes to people who do not have a background in data science. With this, the AI/ML model must be interpretable to communicate its value to management and key stakeholders.

The AI/ML cycle ends with implementing, documenting, and maintaining the project. This includes setting up a batch or API prediction system, documenting the modeling process for reproducibility, and creating a model monitoring and maintenance plan to allow businesses to improve their AI/ML models.

Elevating the Traditional AI/ML Development Life Cycle

While the traditional AI/ML development life cycle ensures business development and value, it fails to include trust into the picture. Standards and protocols for trustworthiness must be present in the design, development, deployment, and management of your AI/ML system.

Konfer empowers businesses to elevate their AI/ML development life cycles by incorporating trust into the process. Konfer operationalizes AI trust through the Konfer Confidence Cloud, a solution that maps, measures, and manages AI-powered applications across all AI/ML development stages. The Konfer Confidence Cloud operationalizes trust by offering a rich set of capabilities:

  • KonferKnowledge – the repository of all AI software assets and associated attributes, including AI/ML applications, models, feature stores, and data
  • KonferConfidence – a quantitative measure that enables businesses to profile and measure AI/ML applications on performance, robustness, privacy, compliance, and regulatory risk factors
  • KonferTrust – a system that generates operational alerts and reports while integrating with an organization’s service management and collaboration systems

Konfer Confidence Cloud helps businesses instill trust into their AI/ML development life cycles to achieve business continuity, improve collaboration, and increase regulatory confidence.

Set up a demo with us today to find out how you can elevate your AI/ML development life cycle.

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