At the foundation of any AI application are the models that power them.
Are you looking for a competitive edge in your industry? By leveraging AI—through robust Data Engineering, powerful Machine Learning, tailored Prompt Engineering, fine-tuned LLMs & RAG, and reliable ML/LLM Ops—we can help you turn complex data into actionable insights, streamline operations, and unlock new revenue streams. Our end-to-end approach ensures your business is equipped with cutting-edge models and the supportive infrastructure needed to innovate at scale and stay ahead of the curve.
AI Models have evolved significantly in the last 10 years, with computing power and cost of storage reaching the point that the models can be trained on vast amounts of data in shorter time periods. This has resulted in an order-of-magnitude leap in capability and enables many more use cases for business and personal use, such as Generative AI and Agent Automation.
- Large Language models (LLMs): Machine learning models that can perform natural language processing (NLP) tasks, such as generating and translating text, answering questions, and identifying data patterns. LLMs are trained on large amounts of data and use deep learning architectures, such as the Transformer, to learn to predict the next word or sequence of words based on context.
- Deep learning (DL) models: A subset of machine learning models that use deep neural networks to learn from large amounts of data. DL models are often used for image and audio recognition, natural language processing, and predictive analytics.
- Supervised learning: A type of machine learning that uses labelled datasets to train models. The model uses the labelled data to discover connections and trends between the input data and the desired output.
- Unsupervised learning: A type of machine learning where the model is not given access to labelled data. Instead, the model must independently identify connections and trends in the data. Often used in combination with other methods to prepare data for more rapid ML techniques.
- Reinforcement learning: A model learns through trial and error by being systematically rewarded for correct output and penalized for incorrect output. Reinforcement models are used in social media suggestions, algorithmic stock trading, and self-driving cars.
Other types of AI models include: Artificial narrow AI (ANI), Artificial general AI (AGI), and Artificial super AI (ASI). These forms of AI are evolving rapidly with a lot of R&D and application development investment.
Creating value from AI for your Business requires the AI model to be built on a scalable data-centric infrastructure, with faster algorithmic design and testing cycles, automated governance, and business processes adapted to capturing value from these models. Also, the right skills are needed.
| AI Technology | Description | Skills Required | Example AI Model |
|---|---|---|---|
| Data Engineering | Focuses on collecting, transforming, and organizing large data sets to ensure they’re clean, reliable, and ready for AI/ML projects. | SQL, Python, ETL tools, big data ecosystems (e.g., Spark/Hadoop), data pipeline orchestration (e.g., Airflow), cloud data platforms, data governance, and warehousing. | A retail demand forecasting model relying on consistent, high-quality transaction data from various sources. |
| Machine Learning | Python or R, ML frameworks (TensorFlow/PyTorch/sci-kit-learn), mathematics, statistics, algorithmic understanding, and domain knowledge. | To improve domain-specific answers, refine large language models via fine-tuning or Retrieval-Augmented Generation. | A computer vision model that identifies objects in images for automated quality control on a production line. |
| Prompt Engineering | Crafts and optimizes instructions (prompts) to guide large language models (LLMs) toward generating specific, accurate outputs. | NLP knowledge, deep familiarity with LLM capabilities/limitations, domain expertise, creativity, and iterative testing of prompt structure. | A GPT-based text summarization system where carefully designed prompts yield concise summaries of lengthy documents. |
| LLM Tuning & RAG | Refines large language models via techniques like fine-tuning or Retrieval-Augmented Generation to improve domain-specific answers. | Advanced NLP expertise, deep learning (transformer) frameworks tokenization, data annotation, RAG methods, embeddings, vector databases, and domain proficiency. | A customer-support chatbot that retrieves relevant product info from a specialized knowledge base and integrates it with an LLM for accurate real-time responses. |
| ML/LLM Ops | Establishes pipelines and best practices for deployment, monitoring, and maintenance of machine learning or LLM models in production. | DevOps fundamentals (CI/CD, containerization, Kubernetes), MLOps frameworks, logging/monitoring tools, cloud orchestration, version control, and model governance. | A recommendation engine continuously retrained and deployed at scale to serve personalized product suggestions while monitoring performance and usage metrics. |
Let’s create AI Models that make strategic sense for your business
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