The development process of an AI (specifically, machine learning) model involves several essential steps.
AI/ML Powered Solutions
During a work discussion, Joe, the CEO, endorsed the idea of AI/ML integration. Kimberly, the CTO, starkly aware of her team’s AI/ML shortcomings, urgently turned to external experts.
- Enable AI in your software product by engaging a team of data science professionals from Intetics.
- Computer vision, natural language processing, and advanced analytics can uncover opportunities for your existing products.
- Get even more value by pairing AI with our established Centers of Excellence—we have Centers for Cloud and DevOps, Data Science and Big Data, Internet of Things, CX/UI/UX design, Geospatial, GIS and LBS solutions, UAV/drones, Low Code, Chatbots and Conversational Intelligence, Robotic Process Automation (RPA), and more.
Data is the foundation upon which AI/ML systems learn, make predictions, and generate insights.
- Training: AI/ML models are typically trained using vast amounts of data.
- Performance and Accuracy: The quality and quantity of the data used to train AI/ML models directly influence their performance and accuracy.
- Bias and Fairness: Data plays a crucial role in ensuring fairness and mitigating bias in AI/ML systems.
- Generalization: AI/ML models aim to generalize unseen data.
- Adaptability and Robustness: AI/ML models need to be adaptable and robust to handle real-world scenarios and changes.
- Data Preprocessing and Feature Engineering: AI/ML models’ performance directly depends on data extraction, transformation, and cleaning quality.
- Iterative Improvement: AI/ML models can be refined and improved through iterations of training, testing, and feedback loops.
This is the most important step toward understanding the business context. Many things will be analyzed, including internal and external factors that drive the decision, challenges to be addressed, desired outcomes, and metrics for implementing an AI-based solution.
To build an effective AI model, it’s mission-critical to understand whether the available data set is sufficient for the identified tasks and challenges. Our team will analyze it in detail, consult with you, and share insights about the available data’s quality.
Should the data prove to be not fully available or inconsistent, Intetics’ team will suggest proceeding with additional data acquisition.
This sub-step presumes the process aims to collect the required data from available sources, both internal and external, to feed the AI model.
*Optional step if the dataset isn’t of the required amount and quality level.
Data processing is a technical process, sometimes automated or semi-automated, designed specifically to collect, transform, and load the data (ETL). Usually, an automated data processing pipeline will be set up in the future.
The data processing stage often includes data labeling, which is the process of identifying raw data (images, text files, videos, etc.) to provide the necessary context for the AI model to learn.
Based on the data parameters and business goals, AI experts will select a few AI/ML approaches to confirm the best methodology with the highest predictable accuracy. It’s very similar to a contest between AI models to prove its efficiency and scalability.
Once the model is selected and trained, it will be tested against the validation data set to prove its effectiveness in the real world.
When the model demonstrates the required KPIs according to the declared business needs, the team deploys it to the customer’s production environment, e.g., a web/mobile application.
The final stage is to continuously improve the AI model’s performance. This includes eliminating “noise” and adding additional parameters to increase the accuracy and computational speed when working with new data.
It’s worth noting that while the above provides a general framework, the development of AI models can be complex, and additional considerations might arise based on the specific context, domain, and use case. Collaboration with domain experts, constant validation, and careful consideration of ethical implications are also essential throughout the process.
Enable Your Product with AI/ML
1. Adding AI/ML features to your existing product may double its usefulness through:
2. All of Intetics’
CoEs are implementing
AI into their
- Enhanced Features and Functionality – prediction, recognition, analysis, personalization, and much more
- Improved User Experience – analyze users’ habits and offer recommendations or shortcuts that are relevant to their needs
- Automation of Routine Task – filling out forms, social media management, and reports can all be automated
- Data Analysis and Insights – predict future sales trends and identify potential issues before they become significant problems
- Cost and Time Efficiency – predictive maintenance, fraud detection, and inventory optimization
- And even New Business Models like advanced analytics and automated insights
Why Trust Intetics with AI/ML Development?
Intetics started working
with AI/ML projects about
20 years ago
Solid team of data
scientists and ML engineers,
accompanied by our own
Developed dozens of
computer vision, predictive
analytics, and NLP models
Established an AI/ML
Center of Excellence
Gathered vast knowledge
and employs experienced
engineers in the field
Intetics AI/ML Technology Practice
Team of 30+ data scientists, machine learning
experts, business analysts, and data engineers
ML Ops to support ML projects of any scale: ML
platforms, data storage, and processing tools
R&D – staying updated with the latest trends,
techniques, and tools in the ML world
Sustainable innovation – Quick PoC
development to confirm/reject the feasibility
of the business case for future transformation
Measurement and Evaluation – a robust system
for measuring and evaluating the performance
of its ML models, as well as the overall
effectiveness of the organization’s ML initiatives
AI/ML Technology Areas
Natural Language Generation
Generative AI for Your Enterprise
Gen AI Model
Pay per Usage
Rapid Prototyping with 3d
Gen AI Model
Using 3d party to connect
internal sources for smart
Gen AI Model
Integrate an open-source or pre-learned
Generative AI engine with internal apps
to train on your specific data
Gen AI Model
Gen AI Model
Custom Gen AI mode development with
enhanced data security
Predictive Maintenance for Manufacturers
Predictive maintenance can foresee potential failures, allowing for scheduled repairs that minimize unexpected downtime.
Regular monitoring and timely maintenance can enhance the operational life of machinery, leading to long-term savings.
Achieving lesser downtime and increased production efficiency leads to increased supply and revenue.
Identifying issues before they result in catastrophic failure helps in maintain a safer working environment.
By addressing issues before they become critical failures, predictive maintenance can save on emergency repair costs and associated production losses.
Predictive analysis helps to better manage of spare parts inventory by predicting when and what parts might be needed.
AI/ML for Different Industries – Healthcare
AI/ML algorithms can analyze complex medical images like CT scans, MRIs, and X-rays to identify patterns invisible to the human eye, thus assisting in early and accurate diagnosis. For instance, they can detect cancerous tumors, as well as signs of stroke, heart disease, and other conditions.
AI algorithms can analyze data from wearable devices to monitor patients’ health status in real time. This allows healthcare providers to intervene promptly when needed, and it also enables more effective management of chronic diseases.
Machine learning models can use data from individual patients to recommend personalized treatment plans. They can account for personal, genetic, and environmental factors that influence the effectiveness of different treatments.
AI-powered chatbots and apps can provide mental health assistance, offer coping mechanisms, track users’ moods and emotions, and even alert professionals when a user is at risk.
AI can process large datasets to predict disease outbreaks, patient readmissions, or other adverse events. This can help public health officials and hospitals prepare and respond more effectively.
AI can predict patient flow and help optimize the allocation of healthcare resources, from staffing schedules to equipment use, improving the efficiency of healthcare delivery.
Machine learning can expedite the drug discovery process by predicting the effectiveness of different compounds, thereby reducing the time and cost involved in bringing new drugs to market.
AI can help identify eligible participants for clinical trials more efficiently and accurately, helping to speed up medical research.
AI-assisted robotic surgery can help perform precise operations, reduce the chance of human error, and potentially improve patient outcomes.
AI can achieve better accuracy in patients’ medical data analysis by allowing medical products and services providers to become compliant (or certify their products or services) with standards from the NHS, U.S. Department of Health and Human Services, etc.
AI can automate the processing and interpretation of medical records, helping to digitize handwritten notes or standardize disparate electronic formats, making it easier for healthcare professionals to access and use this data.