Data Engineering
Data and AI are two sides of the same coin. Using AI when it comes to productivity boosting tools like content creation and basic chat bots is relatively straightforward. However, it requires careful planning, strategizing and above all, good data with context to realize the true transformative power of AI.
Model Selection
A common misconception is that more data yields better results when using AI/ML algorithms. In reality, what truly matters is the quality of cleansed data for creating training sets. The other common misconception is that data preparation is a one-time task. As your Machine Learning models evolve, or newer data becomes available, you will often need to refine your data cleansing steps.
Mantrax can help you with your data preparation process in the following ways:
- Defining your data ingestion process and framework
- Development of Rule-Based Systems to cleanse your data
- Using Foundation Models (like LLMs) to cleanse your data
- Data Virtualization (we are particularly fan of Lakehouse Federation
Design. Deploy. Sustain.
Data ingestion and cleansing, MLOps, etc requires data pipelines and other supporting infrastructure. Whether you choose to build your data pipelines in cloud or on-premise, we have a long history of infrastructure creation and deployment. When it comes to infrastructure, we put a lot of thought into disaster recovery, log monitoring, notifications and operating cost. And yes, infrastructure creation is always done idempotently through scripts (not manually).
