How fast access to data and quality ML code can enable competitive differentiation and innovation

2022 ended with many successful AI models being deployed, including OpenAI’s ChatGPT. There’s no doubt that there will be plenty more successes in 2023. In fact, the global AI market is expected to grow to $1.81 trillion by 2030.

As businesses increasingly rely on AI-driven decision-making, the ability to quickly and easily access and analyze large amounts of data  from various sources is crucial to gaining a competitive advantage. Quality ML code is also essential, as it allows businesses to quickly develop and scale ML applications in production, turning their data into business value faster. 

With fast access to data and quality ML code, businesses can develop data applications that are faster, more reliable, and more efficient than their competitors. This is where solutions like Starburst’s SQL query engine (and its ability to securely access data stored anywhere) and evoML come into play.  

Moreover, McKinsey & Company wrote, “Companies spend 80 percent of their time on repetitive tasks such as preparing data, whereas the actual value-added work is limited.” This is demonstrated in the data science space where companies have to extract data from their numerous databases and centralize them before transferring to other ML systems, which is a slow, labor-intensive and expensive process.

TurinTech and Starburst: unlock full value of your data at speed

TurinTech’s evoML platform is designed to streamline the process of developing and deploying production-quality ML model code, from weeks to days. It offers a range of tools and resources to automate the end-to-end data science lifecycle, including automatic data pre-processing, parallel model code training, multi-objective optimisation, and automatic ML code review and deployment. 

What’s more, it also provides a library of pre-trained models that can be easily customized for specific use cases. This can significantly reduce the time and resources required to generate ML models, allowing teams to meet deadlines without sacrificing quality. The platform also provides AI model code for transparency while enabling the technical team to implement further customizations. 

evoML also provides visual explanations and signals to simplify AI work for non-technical users, enabling them to manage projects autonomously. This can free up specialized data scientists and machine learning engineers for other responsibilities, and increase productivity. 

Starburst shortens the path between the data and the business value, dismantling data silos and making distributed data fast and easy to access. How? Starburst’s query engine can read across data sources on-premise and in the cloud and can replace or reduce a traditional ETL/ELT pipeline. 

Moreover, using Starburst significantly reduces time to insight by removing technical and process constraints while avoiding unnecessary complexities stemming from managing data copies. 

Business users can also rely on Starburst to build domain-specific data products that ultimately garner business value in the form of data analytics. This enables data engineers to focus less on building infrastructure and pipelines, so they can focus more on using simple tools they already know, such as SQL to prepare high-quality, low-latency data products for end users. 

Together,  with TurinTech’s evoML platform, Starburst can elevate data analysis with AI  and empower customers to do more with their data.

How this integration will drive business value and innovation

Speed and agility with respect to data are critical in remaining relevant in the fast changing business world. Once you build a tech stack that enables your teams to access and analyze data faster, you also unlock the keys to ensuring you remain competitive. 

The combination of quick access to all your data and quality ML code can enable organizations to differentiate themselves and drive innovation in a number of ways. For example: 

• Timely insights
  Quickly identify and real-time monitor trends and  patterns that impact business performance to make more informed and timely decisions.
• Product iteration
  Develop new products or services with accelerated time-to-market to meet the changing needs of their customers.
• Process acceleration
  Streamline and automate processes to increase efficiency and reduce costs, such as inventory management and customer service.
• Risk mitigation
  Identify and respond to potential issues, such as financial risks or supply chain disruptions, before they become major problems.
• Identify new markets
  Identify new opportunities for growth and expansion before your competitors.
• Enhance customer experiences
  Gain timely insights from customer data and deliver faster customer services both online and offline.

Starburst and TurinTech together will empower companies to quickly build custom ML models within a database, and deliver ML-predictions to existing business systems. 

By incorporating an ML layer directly into the database, the process from raw data to AI-driven insights will be accelerated from weeks to days. Furthermore, by leaving the data where it resides data security is improved when dealing with the complexities of highly-sensitive data such as in financial services. 

Additionally, this will give companies access to top-quality AI code to future-poof performance and scalability  while simultaneously improving technical debt management. By leveraging the combined power of Starburst and evoML, businesses can differentiate themselves and drive innovation, positioning themselves for success in the long term. 

For more information on this new partnership, visit TurinTech here.