Podcast Summary
Technology Roles: Individuals with diverse backgrounds can succeed in technology, adaptability and continuous learning are crucial for success in technology roles.
Cassie Shum's journey from a biology background to becoming the VP of Field Engineering at Relational AI highlights the evolving nature of technology roles and the importance of adaptability and continuous learning. Cassie shared how her initial fascination with the software behind research programs led her to pursue a master's degree in computer science and eventually a career in software engineering. Her diverse experience at Thoughtworks, where she learned various aspects of technology and leadership, prepared her for her current role at Relational AI, where she applies her skills to implement their AI co-processor for data clouds and language models in customer environments. This conversation underscores the potential for individuals with diverse backgrounds to succeed in technology and the importance of staying curious and open to new opportunities.
Developer productivity with AI: AI tools like Copilot can assist developers but the focus should be on outcomes, not just productivity. Relational AI's AI co-processor aims to help organizations extract insights from large data and accomplish various use cases.
While Generative AI tools like Copilot can assist developers in their work by providing suggestions and quicker understanding of large amounts of data, they are not the end-all solution for boosting developer productivity. Instead, the focus should be on the actual outcomes that engineering organizations aim to accomplish, such as delivering valuable features to customers. The value of developer productivity lies in the ability to produce outcomes that benefit the organization. When it comes to Relational AI's offering of an AI co-processor for data clouds and language models, it aims to address the pain point of extracting insights from large and siloed data by providing an additional tool to help organizations make sense of their data and accomplish various use cases like entity resolution, customer 360, and graph analytics. The AI co-processor is designed to work in conjunction with existing data warehouses, allowing organizations to make the most of their data and gain valuable insights to drive better outcomes.
Relational AI: Bringing AI tools directly to the data instead of moving it allows for data security, governance, and cost savings while enhancing data value and utility
A new industry trend is emerging where instead of moving data out of an ecosystem to apply AI and analytics, the focus is on bringing AI tools directly to the data. This approach, known as relational AI, allows data to remain in its original location while still enabling the use of various AI tools such as business rules, predictive analytics, and prescriptive analytics. This not only saves time, energy, and resources but also removes constraints related to data security and governance. The use of a knowledge graph adds metadata, semantics, and rules to the data, enhancing its value and utility in the modern data stack. This shift towards bringing AI to the data is an exciting development for businesses that have previously felt unable to use AI due to data privacy concerns or the complexity of moving data. While there is still a need for traditional ETL pipelines to handle legacy data, the focus should be on modernizing the data stack and keeping all data in one place with a clear understanding of its nature.
Knowledge graphs for business logic: Using knowledge graphs to bring business logic closer to data increases accuracy, precision, and efficiency, while preventing errors and duplication. Domain-specific modeling and data cleanliness are crucial for effective Relational AI systems.
Bringing essential business logic closer to your data through the use of knowledge graphs can lead to increased accuracy, precision, and efficiency. This approach helps prevent errors and duplication, ensuring that the data AI draws on is specific to your business and domain. Additionally, the importance of domain-specific modeling and data cleanliness cannot be overstated when working with Relational AI systems. Companies like Relational AI provide tools and knowledge engineers to help model concepts within organizations and weave in existing data, enhancing the overall fidelity and effectiveness of the system. Ultimately, the success of any AI solution relies on a strong foundation of clean, domain-specific data.
Field Engineering Role in Product Development: Field engineers bridge the gap between customers and product teams, ensuring customer satisfaction and driving product evolution towards self-service solutions through continuous feedback and collaboration with specialists.
Field engineering plays a crucial role in the continuous development and improvement of a new and innovative product. Field engineers act as the bridge between customers and product teams, understanding the unique needs and ecosystems of each customer while also being experts in the product itself. Their primary goal is to ensure customer satisfaction, making the product journey as easy as possible. However, the ultimate goal is to evolve the product into a self-service, repeatable solution, eventually phasing out the need for close handholding. This feedback loop between customers and product teams is essential for the product's growth and maturity, making field engineering a vital part of the process. Additionally, field engineers work with a team of specialists to stay updated on new technologies and adapt to the ever-changing landscape of the industry.
Advanced analytics implementation: A successful analytics strategy involves a clear focus on outcomes, collaboration between teams and partners, and a commitment to continuous learning and engagement with the community.
Successful implementation of advanced analytics, such as graph analytics, prescriptive analytics, and predictive analytics, requires a collaborative effort between specialized teams and partners. Cassie, an expert in the field, emphasizes the importance of maintaining focus on the desired outcome when applying these technologies. She shares an example of using AI for generating recommendation letters as a fun and exciting application, highlighting the time and effort saved. Furthermore, Cassie underscores the importance of partnerships, with companies like Thoughtworks, to ensure a comprehensive understanding of various customer ecosystems and technologies. Lastly, she encourages engagement from the community, as seen in the recognition of Antoni Muroff for his excellent answer on Stack Overflow, which helped 63,000 people with a similar question. In essence, the key takeaway is that a successful analytics strategy involves a clear focus on outcomes, collaboration between teams and partners, and a commitment to continuous learning and engagement with the community.
Community engagement: Encourage community participation and feedback, listeners' suggestions can lead to valuable discussions, and ratings and reviews help spread the word about the podcast
Learning from this podcast episode is the importance of community engagement and feedback. The hosts acknowledged that they may have overlooked suggestions from listeners to discuss certain topics less, but they encouraged listeners to continue sharing their thoughts and ideas. In fact, some listeners have even contributed to the blog. The hosts also reminded listeners to leave ratings and reviews to help spread the word about the podcast. Cassidy Williams, Ryan Donovan, and Cassie Shum shared their contact information for those who want to reach out directly. Lastly, they mentioned that relational AI is undergoing a website overhaul and the new content will be available soon at relational.ai. Overall, the hosts emphasized the value of active participation and communication within the community.