SCROLL DOWN

Machine Learning

Q: I have a large client facing application, I am worried about the token costs?

During the AI strategy phase we need to carefully evaluate the cost-benefit of implementing AI for all your customer interactions. If the ROI is clear to implement AI for all users, then we can implement methods of smart questions or query caching to reduce the api calls. Several methods exist to reduce token costs.

Q: Will implementing Generative AI require significant changes to our technology infrastructure or systems?

Implementing Generative AI typically doesn’t demand major alterations to existing technology infrastructure or systems, as it operates independently. Its standalone nature allows for seamless integration without disrupting current operations, making it a versatile solution for various applications.

Q: When we utilize AI tools with our data, is our customer information shared, and what level of security is implemented to manage it?

To ensure the security of customer information when using AI tools with our data, implement robust encryption protocols, access controls, and regular security audits. Additionally, adhere to strict data privacy regulations and transparently communicate privacy measures to customers to foster trust.

Q: What is the general cost of developing AI & ML solutions?

Well, it completely depends on a number of factors, including the scope of the project, number of solutions developed, man-hours invested and more. Siam provides time-tested AI consulting to its clientele and assesses the project before initiating to give a ballpark figure to go ahead with.

Q: What steps are being taken to ensure that the Gen AI solutions we implement are ethical, transparent, and aligned with our values as a business?

To ensure ethical, transparent AI solutions aligned with business values, implement robust frameworks, conduct regular audits, and engage stakeholders for feedback and oversight. This approach fosters trust, accountability, and fairness in AI deployment.

Machine Learning (ML) is a subfield of AI focused on creating algorithms that learn from data. Unlike rule-based programming, ML systems learn patterns and make predictions or decisions based on examples. Techniques include regression, classification, clustering, and neural networks.

Application areas

ML models filter spam emails by analyzing features like sender info, keywords, and content. Trained on labeled data, they adapt over time, improving accuracy and keeping inboxes clutter-free.

01

Healthcare

ML predicts disease outbreaks, diagnoses conditions, personalizes treatment plans, aids in drug discovery, and manages patient records, enhancing overall healthcare efficiency and outcomes.

02

Finance

ML powers fraud detection, credit scoring, algorithmic trading, risk management, and personalized financial advice, improving financial security and decision-making processes.

Benefits

Improved Customer Experience

These technologies automate and enhance customer interactions through personalized recommendations, efficient customer support, and sentiment analysis, leading to higher customer satisfaction and loyalty.

Operational Efficiency

By automating repetitive tasks, predicting maintenance needs, and ensuring high-quality data, these tools streamline operations, reduce errors, and improve productivity.

Fraud Detection and Risk Management

ML and NLP detect and prevent fraudulent activities in real-time, while ETL ensures accurate data integration, supporting robust risk management practices.

Scalable Data Management

ETL processes handle the consolidation and transformation of data, providing a solid foundation for ML and NLP applications, which in turn drive advanced analytics and business intelligence.

Cost Optimization

Automation and predictive analytics reduce operational costs by minimizing downtime, optimizing inventory, and enhancing resource allocation.

Cost Optimization

Improved Customer Experience

By automating repetitive tasks, predicting maintenance needs, and ensuring high-quality data, these tools streamline operations, reduce errors, and improve productivity.

Operational Efficiency

ML and NLP detect and prevent fraudulent activities in real-time, while ETL ensures accurate data integration, supporting robust risk management practices.

Fraud Detection and Risk Management

ETL processes handle the consolidation and transformation of data, providing a solid foundation for ML and NLP applications, which in turn drive advanced analytics and business intelligence.

Scalable Data Management

Automation and predictive analytics reduce operational costs by minimizing downtime, optimizing inventory, and enhancing resource allocation.

Cost Optimization

Improved Customer Experience

Techniques of Machine Learning

7 steps to implement

The ETL Layer is a computational process where data is extracted from various sources, transformed into an analysis-friendly format, and loaded into a destination like a data warehouse. ETL, standing for Extract, Transform, Load, is key in data warehousing, consolidating disparate data for business intelligence, reporting, or analytics. It improves data quality and accessibility, allowing businesses to turn raw data into actionable insights, such as a retail company unifying sales data from various stores.