Transform Your Business With AI/ML To Conquer New Horizons!
Be the best in your industry by transforming your business with Think Smart’s Innovative Solutions and AI Implementation Strategy.
Leveraging The Power of AI/ML
Automate your business operations and make well-informed decisions
AI is the key to turning that data into business value. Our developers use AI Experience to significantly change business operations and elevate them to the next level. With AI/ML, you can boost your productivity by focusing on critical tasks, like creative thinking and problem-solving, and gathering important insights from bulk data sets to make data-driven decisions.
AI Implementation
Planning a proper strategy is essential before implementing any new technology that will directly impact your business. Adopting and migrating to AI is meticulous; attentiveness and cautiousness are required to avoid mistakes. Here’s how Think Smart experts will help you devise an effective AI Implementation:
- Data acquisition to understand behavioral patterns.
- Model Development using feature engineering, model training, and model evaluation.
- Integrate an AI Model into the existing environment.
- Monitor the model for further improvements.
Data Engineering
Enterprises recognize that data is one of their most valuable assets. It is the primary motivator for implementing AI solutions that produce predictable results. To ensure quality results, you’ll need the right quality and quantity of data to train your AI models.
- Choosing the best tools for ingesting and processing your data
- Creating and automating data pipelines for your models
- Identifying the need for synthetic data and developing solutions for data generation
AI Transformation Accelerator
The AI Transformation Accelerator is a four-week engagement designed to investigate and assess the potential impact of AI on your organization. The AI Transformation Accelerator service includes the following features:
- Defining your company's problems.
- Developing metrics for improvement and ROI frameworks.
- Creating a solution space comprised of internal/external data sources, model classes, and offline/online technical solutions.
- Making an implementation plan.
- Creating proof-of-concept models based on your existing data.
ML Operations
ML Operations focuses on streamlining the deployment, maintenance and monitoring of the ML Models. It is a useful approach for developing and improving the quality of machine learning and AI solutions.
To handle ML Operations, our experts are well-versed in using the following tools:
ML Feature Monitoring
ML Model Monitoring
ML Model Development
The management of ML models is just as important as the initial build of the model by selecting the appropriate dataset. Model retraining, model versioning, model deployment, and model monitoring are the foundations for machine learning operations (MLOps), which enable data science teams to deliver high-performing models.
Our ML Model Development service will provide accurate, usable results from your models, which will have been built, trained, tested, and deployed using best-in-class open-source and commercial tools.
Our skilled experts use the following tools for the development, testing, and deployment of the ML Model:
Think Smart’s AI Implementation Strategy
Our adept team of experts will build up an AI Implementation Strategy, which will assist you in identifying and realizing the highest value opportunities from implementing AI solutions in your business. Here’s how our experts will help to make a change:
- Determine and identify the ideal use cases to take advantage of the extensive capabilities of AI to resolve your business problems.
- Define success standards by evaluating business needs, data sources and quality, semantic relationships, extraction, and return on investment calculation.
- Test the existing data to ensure the identified requirements can be implemented with the solution; otherwise, the experts clean the data to avoid complications.
- Take inputs and improve the solution from the respective departments in the business organization for efficiently training the AI Model.
- After the successful testing and during the transition to live operation, the experts validate the ROI estimated at the beginning.
