Why: AI Data Audit
Relevance
Assess whether the data being used is actually pertinent to the objectives of the AI system. Irrelevant or outdated data leads to misguiding outcomes. The data audit ensures that the data aligns well with the problems that the AI system is designed to solve, optimizing for efficiency and effectiveness.
Quality
Test the integrity and accuracy of the data used in the AI systems. Poor data quality leads to incorrect predictions or insights undermining the system's reliability and effectiveness. This involves checking for issues like missing values, anomalies, and irrelevant columns that could adversely affect AI training and performance.
Governance
Examine the policies, procedures, and organizational structure that oversee data management. Governance helps ensure that data is ethically sourced and managed, and that there's accountability and transparency in how the data is used. Proper governance ensures that the AI system is built on a strong ethical foundation.
Security
Evaluate the measures taken to protect data from unauthorized access and tampering. This is critical not just for compliance with data protection laws, but also for ensuring that the AI model itself remains reliable. Security lapses compromise data integrity and, subsequently, the AI model's performance.
Testimonials: What Our Clients Are Saying
Without their help, we may have gone out of business. We saw a 1,000% increase over the maximum capacity of our legacy systems, over 48,000 orders per day, and an average of 75 million page views and 2.3 million unique users each month in 2017.
Asif is a brilliant computer scientist, a physicist, an original thinker, an erudite scholar, and an artist who can sculpt new algorithms, and paint new solutions, veritably a Leonardo da Vinci of our times, and a passionate educator with deep insight into AI/ML/DL/Data-Science/Big-Data topics.
When it comes to Big-data and machine learning, I would simply say he (Asif) knows A-Z in that. No matter what the domain is, he has machine-learning-based solutions for the problems involved. Working with him for years, I have realized how his predictions on the machine learning revolution in big data have come true.
We’ve done development in the past..the SolutionMap is a higher level way of defining a comprehensive approach to getting the success that you want.
It was not just about software…it was all about what is this going to do in your company, how are the people in your organization with your partners and with your other stakeholders going to interact with this piece of software and what are their expectations of it…it also helped us on the backside thinking about our business processes…it was really illuminating to me.
From the design through to the development detail, they listened, understood, and carefully tested before release, often pushing our initial ideas to a better outcome.
