DS and AI Strategies

Solutions

• DS and AI Strategies
  • Economics of AI - Costs
  • Implementation
  • DS Excellent Characteristic

Economics of AI - Costs

1. Take a multi-use case approach to getting started with Enterprise AI. This apporach can reduce the costs such as:
   1. Reuse is the simple concept of avoiding rework in AI projects, from small details (like shared code snippets to speed up data preparation) to the macro level (like ensuring two data scientists from different parts of the company aren’t working on the same project).
   2. Capitalization takes reuse to another level — it’s all about sharing the costs incurred from an initial AI project across other projects, resulting in many use cases for the price of one, so to speak.

   Capitalization means that while tackling these larger, higher-priority use cases, the organization can also take on lots of other smaller use cases by reusing bits and pieces, eliminating the need to reinvent the wheel with data cleaning and prep, operationalization, monitoring, and more. It can also spurr the discovery of hidden use cases.

   

2. List of Cost of Enterprise AI:
   1. Data Cleaning and Preparation
   2. Operationalizing and Pushing to Production
   3. Data Scientist Hiring and Retention
   4. Model Maintenance
   5. Complex Technological Stacks
3. Obstacles that IT organizations must overcome to adoption of AI:
   1. Modernizing infrastructure without sacrificing legacy investments.
   2. Adapting to seasonal demand, data volume, and changing business demands.
   3. Enabling more users across the organization with various skill sets as demand accelerates.

Implementation

1. Build an Adaptive and Future-Proof Analytics System
   • an adaptive platform should be able to handle an increasing amount of computing power, in any configuration, without any degradation in performance.
   • Flexible, Adaptive, autoscaling and Self-service analytics
   • Breaking down AI initiatives into modular components allows for flexibility and scalability.
   • Encouraging a culture of experimentation, innovation, and democratization is vital for scaling AI
2. Achieve more with less and accelerate your time to value with AI
   • Accelerate the process and make AI a collaborative effort by fostering data-driven teamwork and tapping into industry expertise.
   • Three key steps:
     • Implement MLOps to taking a whole lifecycle approach to develop and deploy AI models.

     

     • Speeding data democratization without going off the track. How to building and easily sharing data assets with proper control, permissions that well-defined, efficiently managed, permissions at the asset level to allow a simplified and secure use of the data objects.

     • Start small with clear direction, developable and measurable ROI.

   • Encourage cross-functional collaboration
   • Establish agile workflows
   • Invest in AIOps skill development
   • Leverage pre-trained models and automl
   • Build accelerators: Identify the areas that need a lot of human intervention and wrap a process around it to separate out the reusable components.
3. Utilizing effective steering instruments, monitoring performance, and promoting transparency
   • data governance and privacy
   • Model explainability and interpretability
   • Continuous monitoring and bias mitigation

DS Excellent Characteristic

1. Consistency - Minimal variance between environments (i.e. using containers)
2. Flexibility - Can accommodate most frameworks
3. Reproducibility - Can recreate past experiments/training
4. Reusability - Components are reusable across projects
5. Scalability - Able to scale resources to efficiently meet demand
6. Auditability - Logs, versions and dependencies of artifacts are available
7. Explainability - Decision transparency
8. Velocity - Need for rapid experiementation, prototyping, and deployment with minimal friction
9. Validation - Need for checks on quality and integrity of data, features, models, and predictions
10. Versioning - Need to keep track of deployed models and features to ensure provenance and fallback options