- What is Enterprise AI
- Introduction: A New Technology Stack
- Requirements of the New Enterprise AI Technology Stack
- Reference AI Software Platform
- Awash in “AI Platforms”
- “Do It Yourself” AI?
- The Gordian Knot of Structured Programming
- Cloud Vendor Tools
- C3 AI Platform: What is Model-Driven Architecture
- Platform Independence: Multi-Cloud and Polyglot Cloud Deployment
- Conclusion: A Tested, Proven AI Platform
- Enterprise AI Best Practices
- Enterprise AI Buyer’s Guide
- 10 Core Principles of Enterprise AI
- IT for Enterprise AI
- Develop AI 26X Faster on AWS
- Develop AI 18X Faster on Azure
- Enterprise AI Resources
IT for Enterprise AI
IT Skills for Digital Transformations
With the rapid extinction of corporate data centers and the shift to public clouds providing managed cloud services, concepts such as Serverless and NoOps are rapidly becoming a reality. Staffing IT teams to support a corporate Digital Transformation requires significant re-training and attracting, hiring, and retaining individuals such as data scientists with specialized skill sets.
The table below contrasts the skills of current corporate IT teams and the transformation required to design, develop, and operate next-gen AI applications.
Today
Data Center Management
- Networking, Storage, UNIX, virtualization
RDBMS/Datawarehouse
- Oracle, Sybase, Netezza, Teradata, Exadata
Application Development - Java, C#, WebSphere, WebLogic, Ruby/Rails, Force, Spring
Enterprise Applications
- SAP, Oracle, Infor
Digital Platforms
Cloud Platforms (Public/Private/Hybrid)
- AWS, Azure, GCP, Kubernetes, Mesos, Docker
- CPU/GPU/FPGA
Connected Products/IoT
- Gateways, Embedded systems
Relational, Multi-Dimensional and Distributed Data Stores
- HDFS, S3, NoSQL, Columnar, Graph, PostgreSQL, HBase, RedShift, DynamoDB
Next
Conclusion

