At Ameya Edge, we harness the power of AI to drive business innovation and reshape education for a smarter tomorrow. Our solutions help businesses make smarter, faster decisions to streamline operations, boost productivity & improve customer experiences.
HOME
Generative AI & Machine Learning
Data is the fuel, but AI is the engine. Ameya's Generative AI & Machine Learning program is an intensive, end-to-end journey designed to turn data professionals into AI architects. We don't just teach you how to use tools; we teach you the underlying mathematics and logic that power Predictive ML and the transformer architectures that drive Generative AI. From cleaning raw data and training Scikit-Learn models to deploying custom LLMs and diffusion models, this course covers the full spectrum of the modern AI landscape.
Our Training Services — Comprehensive Intelligence Curriculum
An end-to-end curriculum covering both predictive ML and the transformer architectures that drive Generative AI.
Master the core algorithms — Linear Regression, Random Forests, XGBoost, and K-Means — using industry-standard libraries to build high-accuracy predictive systems for structured business data.
Understand the “brain” of AI. Learn to build and optimize multi-layer perceptrons and CNNs for complex pattern recognition while mastering backpropagation and gradient descent fundamentals.
A deep dive into the technology behind GPT. Learn about self-attention mechanisms and why they changed AI forever by exploring the evolution from RNNs to modern attention-based modeling.
Practical sessions on taking base models (Llama, GPT, or Stable Diffusion) and specializing them for niche industry data to achieve superior performance on domain-specific tasks.
Learn to move from a notebook to a production environment using Azure Machine Learning pipelines and versioning ensuring your models remain scalable, maintainable, and monitorable.
Bridging the gap between code and ROI — identifying which problems require classic ML and which need Generative solutions to optimize resource allocation and project success rates.
Our Training Process — From Data Points to Intelligent Agents
A structured journey from raw data to production-grade AI systems that actually ship.
Before the AI, comes the data. We start with advanced feature engineering and data synthesis techniques to ensure your models are trained on high-quality, representative data sets.
Learn the “Ameya Framework” for choosing between traditional ML (for precision/prediction) and GenAI (for creation/interaction) minimizing development time by selecting the most efficient tool for the job.
Work in integrated environments (Jupyter, VS Code, and Azure AI Studio) to build hybrid applications mirroring the professional developer's multi-tool workflow.
Intensive training on model evaluation — learning how to measure accuracy in ML vs. “Helpfulness” and “Factuality” in GenAI using rigorous statistical methods and automated benchmarking.
Hands-on experience containerizing models and deploying them as scalable web services leveraging Kubernetes and serverless architectures for maximum uptime.
A final module on bias detection, data privacy, and the long-term societal impact of your AI builds to ensure your solutions are compliant with emerging global AI regulations.
Why Choose Ameya for GenAI & ML?
The Balanced Approach — combining predictive ML with generative innovation.
Most courses teach either ML or GenAI. We teach both, because real-world enterprise problems usually require a Hybrid AI approach that combines predictive analytics with generative creativity.
We ensure you understand why a model works. This “under-the-hood” knowledge allows your team to troubleshoot issues that standard “Prompt Engineers” cannot and adapt models when off-the-shelf solutions fail.
Learn to build AI within a “walled garden.” We focus on data residency, encryption, and keeping your proprietary data out of public training sets to protect your organization's intellectual property.
We teach you how to build “Lean AI” — optimizing for performance while minimizing the high compute costs associated with large models ensuring your AI initiatives are financially sustainable.
Our labs aren't generic; we use datasets from Finance, Healthcare, and Retail to ensure the skills are immediately transferable to your business providing you with a ready-to-use portfolio of relevant solutions.
Master both predictive ML algorithms (Regression, Random Forests, XGBoost) and Generative AI architectures (Transformers, LLMs, Diffusion)
Build, fine-tune, and deploy production-grade models using Azure Machine Learning pipelines and MLOps best practices
Apply the Ameya Framework to choose between traditional ML and GenAI for any real-world business problem
Deploy scalable AI services with Kubernetes, serverless architectures, and modern containerization
Audit AI solutions for bias, data privacy, and compliance with emerging global AI regulations
Equip your team with the skills to predict the future and create it simultaneously.
