Gen AI & Data Science Projects
Your projects a reality: From Lab to Production
Turning Data Science and ML projects into practical, impactful solutions. Embedding AI into your business processes for tangible outcomes.
AI Today & It’s Short Term Future
The promise of artificial intelligence (AI) has long captivated us. But here's the reality: most AI projects fail. In reality, less than 15% of AI projects are deployed into production.
AI projects share more similarities with business initiatives than with mere technological implementations, and some say they are more intended for marketing strategies. But this is changing…
The year 2023 was the year of Proof of Concepts (PoCs).
2024 will be the year of transition to Production.
Our Approach To Success
Our methodology encompasses the entire lifecycle of any AI project. It focuses on AI governance, from analyzing and validating ROI in accordance with the organization's maturity to testing, evaluating, and implementing the designed solutions.
AI projects are strategic business investments. We work with your team to identify high-impact use cases that directly affect your bottom line. Our collaborative approach involves rapid experimentation sessions to refine ideas and build proof-of-concepts (PoCs) for the most promising ones.
Our focus is on efficiency: 80% of ideas are eliminated during these sessions, ensuring we pursue only the most cost-effective solutions with high potential for improvement. PoCs are concise and focused on validating core functionalities, avoiding unnecessary integrations or business rules.
Evaluation is critical. We assess the state-of-the-art AI for suitability, data quality, regulatory compliance, and process efficiency. We're not afraid to stop a PoC if it's not feasible, viewing this as a learning opportunity to refine future investments.
SunnyData delivers responsible PoCs, focused on true feasibility, not marketing hype. We believe AI projects should be approached strategically, maximizing your return on investment.
How can we help?
A few examples…
Our practical approach to evaluation of data science projects, building out prototypes, and testing them allows customers to find insights from their data science projects. Our focus on helping customers move from ideation to prototype to production on these highly sophisticated ML operations allows customers to realize value on their data science programs.
Evaluation and business case development to support ML/AI projects.
Exploration of historical data to uncover trends and inform decisions
Develop prototypes and POC in concept in Databricks and supporting ML Ops platforms for specific use cases.
Validate ROI and production feasibility of prototypes and path to production.
Creation of ML pipelines with continuous integration and delivery
Integration of ML tools like model registries, feature stores confirming to devops, ML and governance standards
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