Driving Tangible Business Results with AI Model Development Services
Cloudsmate enables structuring data into actionable insights while preceding automated operational efficiency and creating AI models.
ML Pipelines
Configuring ML pipelines for creating an AI model with data processing, scoring pipeline feature management, and orchestrating training based on cognitive services.
AI Model Development
As an AI expert development company, Cloudsmate creates intelligent AI models that utilize the power of advanced algorithms and strategic techniques.
AI Model Training
Our experts closely instruct the AI model and attune the forecasting and prediction to the necessities of AI-centric businesses with our AI model training.
Media and Content Annotation
Identifying and tagging media and content, such as images, videos, and text, for data annotation and training an AI model enables accurate predictions and recognition of patterns.
ML Algorithm Tagging
Assigning relevant metadata tags during custom AI model development to unstructured data to make it searchable with key terms related to the machine learning algorithm tagging process.
Fine-Tuning Language Model
Utilizing the fine-tuned language model ensures the gain of valuable insights from textual data, improves communication, and automates language tasks.
Our AI Model Development Process
Deciphering the AI model development process to build intelligent and cognitive models with business-altering capabilities.
1. Identification of Concept
Conducting the business needs evaluation to classify, regress, and recommend the scope of the AI model.
2. Identifying And Collecting Data
To ensure model accuracy, identify the correct data from structured, unstructured, static, and streaming data.
3. Preparing The Data
Cleansing the accumulated data and transforming it into a required format to maintain quality during AI model development.
4. Model Building & Training
Our ML engineers define the model features and algorithm for accurate outcomes and predictions.
5. Model Testing
Training and validation sets are used to test and tune the AI model to improve real-world functioning further.
6. Model Deployment
Ensure the achievement of objectives and KPIs and later deploy the AI model into the intended infrastructure.
