AI & ML OPS
Turbocharge your GenAI and AI/ML initiatives
- Risk Analytics
- Cognitive Services
- Document, Audio & Video Processing and analytics
- NLP & Sentiment analytics
- Open AI, LLM
- Cognitive Services, CV
- ML Services
EDW / DWH MODERNIZATION
Modernize your Data Estate with Azure Data Platform
- Re - Engineer EDW Workloads
- Enterprise DBs to Cloud DWH
- On-prem to Azure Migration
- Spark Data Engineering
- SQL Server, Oracle, Hadoop, MySQL, PostgreSQL
- Azure Synapse Analytics
- Microsoft Fabric
- Azure Databricks, Snowflake
How do I use data insights to grow revenue and increase profitability?
How do I enable data driven insights without significant upfront investment?
How do I increase brand loyalty & customer retention?
How do I enable data driven insights without significant upfront investment?
How do I manage risk?
Data Analytics Consulting Services
Empowering organizations with data analytics consulting to enable data driven decision making.
Power business decisions leveraging cloud data platforms, business intelligence, reporting, dashboards, and advanced analytics.
Progressing from Foundation to Cloud Scale Analytics
- Analyze Data Sources, ETL / ELT Tools, performance and Non functional requirements Needs – perform Rationalization
- Identify and prioritize business needs with KPIs and unify underlying Analytics Capabilities Engine
- Build Distributed Architecture using cloud Data Services and establish Centralized GRC (Governance, Risk and Compliances) Framework tailor-made for business
- Agile development / deployment of Analytics Platform replacing legacy components with unified solution built on Distributed Architecture foundation and addressing business needs
- Establish Scaling Principals driven by Business needs and other factors
- Distributed architecture with centralized governance
- Data Engineering
- Data Lake
- Data Warehouse
- Data Lakehouse
- Business Intelligence reporting
- Self Service Analytics
- Enterprise BI adoption
- Dashboards
Govern, manage, and protect data estate
- Break down Data Silos and Fragmentation - Create holistic view of data landscape by combining Azure Data Technologies in cost-effecting and integration enabled paradigm.
- Resolve Data Quality and Trust Issues - Ensure that the data is accurate, complete, consistent, and reliable. Enable data validation, cleansing, enrichment, and certification.
- Enhance Data Security and reduce compliance risks - Protect data from unauthorized access, misuse, or breach. Enable data encryption, masking, anonymization, and auditing. Comply with regulations and standards such as GDPR, PCI DSS, etc.
- Enable Data Discovery and Resolve Accessibility Challenges - Make data easily discoverable and accessible by the data consumers. Enable data cataloging, indexing, searching, and lineage. Provide data context, meaning, and usage.
ML Advisory
- AI Strategy & Roadmaps
- AI Workshops
- Data Assessment for AI readiness
- AI/ML Product Management
MVP and Proof of Value
- NLP/NLU Modelling
- Deep Learning Models
- Computer Vision Modelling
- Multi model networks
- AI Integrations for IoT and Streaming Data
ML Engineering
- Data Engineering
- AI Powered Analytics & Insights
- AI Architecture & Design
- MLOps & Automation
- Testing & Validation
Solution Partner
- End-to-End AI-powered Product Development Solutions
- Re-engineer EDW Workloads- Improvise performance by using Cloud Data Services for better performance, lower costs and integration
- On-prem to Cloud Migration - Migrate On-Prem compatible Databases to Cloud and manage TCO well
- Spark Data Engineering - Perform near-real-time and batch data processing using Spark Engine in Cloud through Azure Synapse, Microsoft Fabric and Databricks
- Enterprise DBs to Cloud DWH - Modernize Oracle and other Enterprise DBs to unified DWH in Azure with other services and optimize overall technology spread
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