Empowering Organizations with Advanced Machine Learning Capabilities
Empowering Organizations with Advanced Machine Learning Capabilities
With our collaboration with AWS, enterprises can gain deeper insights from their data while simultaneously reducing costs. At every step of the ML adoption journey, we leverage our partnership with AWS to assist with a comprehensive array of artificial intelligence (AI) and ML services, infrastructure, and implementation resources. Their offerings in the field of Artificial Intelligence & Machine Learning empower organizations to develop highly tailored solutions based on advanced machine learning algorithms.
Moreover, we specialize in seamlessly integrating these algorithms with image and video analytics. Additionally, we stay at the forefront of emerging technologies, such as augmented reality and virtual reality, by leveraging our collaboration with AWS. This allows us to provide the utmost customer satisfaction and help our clients gain a competitive edge over others.
With our collaboration with AWS, enterprises can gain deeper insights from their data while simultaneously reducing costs. At every step of the ML adoption journey, we leverage our partnership with AWS to assist with a comprehensive array of artificial intelligence (AI) and ML services, infrastructure, and implementation resources. Their offerings in the field of Artificial Intelligence & Machine Learning empower organizations to develop highly tailored solutions based on advanced machine learning algorithms.
Moreover, we specialize in seamlessly integrating these algorithms with image and video analytics. Additionally, we stay at the forefront of emerging technologies, such as augmented reality and virtual reality, by leveraging our collaboration with AWS. This allows us to provide the utmost customer satisfaction and help our clients gain a competitive edge over others.
We have expertise in
Clustering
Time Series Forecasting
Anomaly Detection
Recommender Systems
Artificial Neural Networks
Digital Signal Processing
Key Offerings
Consulting
- ML proof of concept and ML library assessment
- Solution architecture consulting
- ML tools and hosting assessment
- Model re-engineering advisory
Solution Design and Implementation
- Development of ML model driven AIoT (Edge, Cloud) applications for computer vision or NLP, including training, performance tuning, and testing
- Algorithm porting and deployment services
- ML data annotation for training and validation purposes
Managed Services
- Model-as-a-Service offering
- ML lifecycle management services
- Performance monitoring and optimization support
Our Artificial Intelligence Models
Our Machine Learning Projects Lifecycle
Features of Machine Learning services
Features of Machine Learning services
- Availability of open-source libraries and frameworks
- Inclusion of optimized algorithms for in-database usage
- Flexibility in choosing deployment options
- Inclusion of model explanation capabilities
- Ease of accessing and manipulating data
- Support for multiple scripting languages
Benefits of Machine Learning services
Benefits of Machine Learning services
- Improved prediction and forecasting accuracy
- Enhanced data analysis and pattern recognition capabilities
- Automation of repetitive tasks and increased operational efficiency
- Personalized recommendations and customer experience
- Advanced fraud detection and cybersecurity measures
Why Choose Us?
Meet your industry needs through People Tech AI-ML Data Solution
Case Studies
AI/ML Techniques in Healthcare
Addressing Challenges and Transforming Patient Care
Challenges
Improper or incomplete patient history: Lack of comprehensive patient history hampers accurate diagnosis and treatment. The absence of electronic medical records further complicates this issue.
Digitization of existing medical records: Converting paper-based medical records into electronic formats presents its own set of challenges, requiring time and resources.
Redoing tests due to the absence of patient health records:Without a proper history or patient health record system, medical tests may need to be repeated, leading to increased costs and patient inconvenience.
Shortage of healthcare professionals: The scarcity of registered doctors, physicians, and practitioners can overload the healthcare system and reduce the quality of care provided.
Insufficient skilled support staff and technicians: A lack of adequately trained support staff and technicians affects the overall efficiency and effectiveness of healthcare organizations.
Limited diagnostic tests and facilities: The availability of diagnostic tests and facilities may be limited, leading to delays in diagnosis and treatment.
Handling a large volume of patients: The organization’s capacity may be exceeded, resulting in longer wait times and compromised quality of care.
Recommended Framework
To tackle the challenges, the following framework is proposed:
Manned health kiosks:
Central synchronized data repository: A secure repository is established to store and process patient data, enabling the creation of electronic health records (EHRs) for each patient.
Monitored rule engine: An AI model is implemented to ensure optimal performance and periodic review to maintain model accuracy and learning.
Doctor decision support system: A touch screen pad with an intuitive user interface is provided to doctors, offering a consolidated view of patient information and suggesting diagnoses based on symptoms.
Confidentiality, integrity, and availability: The entire system is cloud-based, encrypted, and managed by a single entity to ensure patient data security and system availability.
AI as a Powerful Tool and Partner
Unlocking the power of big data: AI enables the analysis of vast amounts of patient data, providing valuable insights for personalized and targeted treatments.
Supporting evidence-based decision making: AI assists healthcare professionals in making informed decisions by analyzing data and providing evidence-based recommendations.
Coordinating care and fostering communication: AI facilitates seamless communication and collaboration among healthcare providers, ensuring continuity of care and improved patient outcomes.
Improving
Patient experience and outcomes: AI technologies enhance patient experience by streamlining processes, reducing waiting times, and personalizing care plans, ultimately leading to better treatment outcomes.
Delivering value and reducing costs: By optimizing workflows and resource allocation, AI helps healthcare organizations deliver value-based care while minimizing costs.
Improving health system performance & optimization: AI-driven analytics and predictive modeling optimize resource allocation, reduce inefficiencies, and enhance overall health system performance.
Human-Machine Partnership in Healthcare
Ensuring patient safety: AI systems should undergo rigorous testing and validation to ensure they do not compromise patient safety or well-being.
Enhancing efficiency and productivity: AI automates routine tasks, enabling healthcare professionals to focus on complex cases and providing more efficient and productive care.
Improving usability: User-centric design principles should be applied to AI systems, making them intuitive and easy to use for healthcare professionals.
Facilitating access to care: AI technologies can bridge the gap in healthcare access by providing remote consultations, telemedicine services, and decentralized care models. Controlling costs: AI-driven optimization helps healthcare organizations identify cost-saving opportunities and manage resources more effectively.
AI-Powered Automation
Several AI technologies are transforming healthcare practices:
Medical robotics: Robotic systems assist in surgical procedures, rehabilitation exercises, and medication administration, enhancing precision and reducing human error.
Machine learning: Supervised, unsupervised, and reinforcement learning algorithms can analyze medical data to identify patterns, predict outcomes, and improve diagnostic accuracy.
Natural language processing (NLP): NLP techniques enable the extraction of relevant information from clinical notes, facilitating faster and more accurate analysis.
AI voice technology:Clinical voice documentation and voice nursing assistants streamline data entry and improve communication between healthcare professionals and patients.
