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AI at Scale

ETH_Zurich_090320A
[(ETH - Zurich) - Gian Marco Castelberg]

 

- Overview

AI decision-making is revolutionizing business intelligence by analyzing vast datasets to improve decision-making processes. Machine learning (ML), natural language processing, and computer vision are key components of AI that aid in faster and more accurate decision-making. 

Recent developments in the field of artificial intelligence (AI) demonstrate the scale and power of the technology for business and society. However, businesses need to determine how to build and manage these systems responsibly to avoid bias and errors, as the scalability of AI technologies can have costly impacts on business and society. 

As your organization applies machine learning (ML) and automation to workflows using disparate datasets, it's important to have the right guardrails in place to ensure data quality, compliance, and transparency within AI systems.

 

- Scaling AI

Scaling AI refers to how deeply and widely AI is integrated into an organization's core product or service and business processes.

AI scaling is the process of improving AI systems' ability to handle large workloads, process more data, and become more efficient. It allows AI models and algorithms to adapt to the increasing demands placed on them. 

Some technical enablers for organizations to scale AI successfully include:

  • Using code assets
  • Incorporating data products such as feature stores
  • Implementing standards and protocols
  • Harnessing the technology capabilities of ML operations (MLOps)


Some other ways to make AI models scalable and reliable include: 

  • Choosing the right framework
  • Optimizing code and data
  • Using cloud computing and containers
  • Implementing monitoring and logging
  • Applying version control and testing
  • Adopting continuous integration and deployment


According to a LinkedIn article, companies that capture the most value from AI follow the 10-20-70 rule: 

  • 10% of their AI effort goes to designing algorithms
  • 20% to building the underlying technologies
  • 70% to supporting people and adapting business processes

 

- Machine Learning: From Data to Decisions

Machine learning (ML) is a type of artificial intelligence (AI) that can help with decision-making. ML can: 

  • Analyze data: ML can quickly process large amounts of data and events. It can also analyze data on customer interactions, preferences, and purchasing behavior.
  • Learn from patterns: ML can automatically detect patterns in data and learn from historical decisions and influencing factors.
  • Make predictions: ML can use patterns in data to make predictions about future events.
  • Reduce bias: ML can help reduce human errors and biases


AI decision-making processes can help businesses make faster, more accurate, and consistent decisions. AI can use technologies like ML and cognitive computing to:

  • Analyze large amounts of data
  • Identify patterns and trends
  • Predict outcomes
  • Minimize human biases
  • Offer impartial insights
  • Automate specific tasks
  • Make decisions more quickly than humans


AI can help decision makers in complex scenarios, such as strategic planning or medical diagnosis. 

Here are some steps to an AI strategy for a business: 

  • Start with the right problems
  • Define the business outcomes
  • Collect and organize data
  • Choose the right technology


AI can also help business teams focus better on work relevant to their field.

 

[More to come ...]



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