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Machine Learning Services
Accelerating AI with Smart, Structured, and Scalable Data Ops
At Han Digital,
Our Machine Learning (ML) services are designed to accelerate the end-to-end model lifecycle from data ingestion and preprocessing to training, fine-tuning, deployment, and monitoring. We partner with enterprises, startups, and AI labs to develop scalable ML solutions that solve real business problems across industries.
We specialize in data operations that power the full machine learning lifecycle — from sourcing raw image data to annotating datasets, moderating content, structuring technical assets, and managing training pipelines. Whether you're developing computer vision systems, generative AI, or domain-specific LLMs, we provide the data foundation your models need to perform with precision and context.
Our expertise goes beyond model building we specialize in crafting data pipelines, AI-ready datasets, and domain-specific model tuning that power smarter, faster, and more contextual AI outcomes.
Popular ML Platforms We Work With
As part of our ML Data Operations services, we support integration with leading machine learning platforms to help clients accelerate experimentation, model training, and deployment.
Collaborative, Cloud-Based Python for ML Prototyping
Google Colaboratory (Google Colab) is a cloud-based platform that lets users write and execute Python code directly in the browser. It’s especially popular among data scientists and ML practitioners for its ease of use and built-in support for popular frameworks like TensorFlow, PyTorch, Keras, and OpenCV.
- No setup required — runs in a Jupyter notebook environment
- Free access to GPUs/TPUs
- Easily import data from Google Drive, GitHub, and other sources
- Ideal for rapid prototyping, data exploration, and educational use
- Supports seamless code sharing and collaboration
Fully Managed ML Lifecycle Platform
Amazon SageMaker is a powerful, fully managed service that simplifies the entire ML workflow from data preparation and model training to tuning and deployment.
- Built-in tools for data cleaning, feature engineering, and model training
- Supports leading ML frameworks (TensorFlow, PyTorch, XGBoost, etc.)
- Includes pre-built algorithms and notebook environments
- Offers automatic model tuning (hyperparameter optimization)
- Scalable model hosting and deployment with version control
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- Integration with S3, AWS Lambda, CloudWatch, and other AWS services
No-Code + Code-First ML Platform for Enterprises
Azure ML Studio is a flexible platform offering both drag-and-drop tools and advanced scripting support to build, test, and deploy predictive models.
- Visual workflow designer for users with no coding experience
- Supports R and Python scripting for advanced ML tasks
- Includes a library of built-in algorithms and statistical functions
- Models can be deployed as web services for real-time or batch scoring
- Integrated monitoring, versioning, and management tools
- Compatible with Power BI, Azure Data Lake, and enterprise data sources
We Help You Choose the Right ML Stack
As part of our Digital Operations and ML Data Enablement services, HAN Digital supports platform-agnostic model development and helps teams prepare AI-ready datasets that integrate seamlessly with platforms like Colab, SageMaker, and Azure ML Studio.
