Embedding AI models into existing business workflows
Building robust ML inference infrastructure
Ensuring models run efficiently at scale
Tracking model performance and data drift
Setting up complete machine learning operations
Establishing policies for responsible AI use
Version control and deployment orchestration
Systematic approach to model development
Automated insight discovery in BI platforms
AI-assisted data cleaning and transformation
Anomaly detection in KPIs and metrics
Forward-looking analytics in BI tools
Conversational interfaces for data access
Intelligent data organization and indexing
Automated data classification and metadata generation
AI-driven data quality and compliance monitoring
1.
Assessment
Identify where AI can enhance existing data infrastructure
2.
Pilot Development
Proof of concept with measurable success criteria
3.
Integration
Seamless embedding into current systems
4.
Training
Enable your team to manage and evolve AI capabilities
5.
Optimization
Continuous improvement based on results
MLflow, Azure ML Pipelines
TensorFlow, PyTorch, Azure OpenAI
Python, R, SQL, Jupyter, Azure ML Studio, Databricks Notebooks
