https://store-images.s-microsoft.com/image/apps.10251.1b01787d-18bd-438e-92de-65204470aa48.e16b0bfa-803f-49c6-917b-a41311346425.6a0186e0-0546-4be4-801c-e91d356ab639
Predictive Analytics Jumpstart w/Synapse (4wk POC)
Hanu
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Predictive analytics has rocketed at the top of the boardroom agenda, with companies envisioning value across functional domains and enterprise-wide processes.
However, these companies still find it a challenge to make predictive analytics work for them due to their legacy & siloed IT infrastructure, capability gap to unlock the power of data to solve critical business problems and the complexities involved in moving to a new environment.
Our team of certified specialists will guide you through a 4-week engagement to accelerate your predictive analytics journey with Azure Synapse Analytics by helping you answer four core questions:
- What are the predictive analytics implementation challenges and how can I mitigate them with proven best practices?
- How can I showcase success and vision of these initiatives to the leadership?
- What should be the implementation roadmap and estimated costs?
- What will the post migration architecture look like?
Workshop delivery process
Week 1
- Assess data environment
- Create high-level roadmap and system architecture
Week 2 and 3
- Setup Azure Infrastructure for POC
- Enable data ingestion
- Train Machine Learning Models and test results
- Validate results and build visualizations
Week 4
- Conduct Workshop to educate team on implementation process, pain points and mitigation plan
- Socialize POC results
- Knowledge Transfer: Codes and all artifacts for next level references
Final deliverables
- High level post migration architecture
- Migration estimates and budget
- POC code and all artifacts for reference
Overblik
https://store-images.s-microsoft.com/image/apps.19589.1b01787d-18bd-438e-92de-65204470aa48.e16b0bfa-803f-49c6-917b-a41311346425.086b28a3-a471-47d3-a104-8a9e0c1d4180
https://store-images.s-microsoft.com/image/apps.14393.1b01787d-18bd-438e-92de-65204470aa48.e16b0bfa-803f-49c6-917b-a41311346425.1f86453a-789b-48a5-9e21-56ca071803e3