Away From the Keyboard: Apoorva Joshi, MongoDB Senior AI Developer Advocate

Evan Alger and Casey Stegman
September 26, 2024 | Updated: October 29, 2024
#Culture

Welcome to our article series focused on developers and what they do when they’re not building incredible things with code and data.

“Away From the Keyboard” features interviews with developers at MongoDB, discussing what they do, how they establish a healthy work-life balance, and their advice for others looking to create a more holistic approach to coding.

In this article, Apoorva Joshi shares her day-to-day responsibilities as a Senior AI Developer Advocate at MongoDB; what a flexible approach to her job and life looks like; and how her work calendar helps prioritize overall balance.

Q: What do you do at MongoDB?

Apoorva: My job is to help developers successfully build AI applications using MongoDB. I do this through written technical content, hands-on workshops, and design whiteboarding sessions.

Q: What does work-life balance look like for you?

Apoorva: I love remote work. It allows me to have a flexible approach towards work and life where I can accommodate life things, like dental appointments, walks, or lunches in the park during my work day—as long as work gets done.

Q: Was that balance always a priority for you or did you develop it later in your career?

Apoorva: Making work-life balance a priority has been a fairly recent development. During my first few years on the job, I would work long hours, partly because I felt like I needed to prove myself and also because I hadn’t prioritized finding activities I enjoyed outside of school or work up until then.

The first lockdown during the pandemic put a lot of things into perspective. With work and life happening in the same place, I felt the need for boundaries. Having nowhere to go encouraged me to try out new hobbies, such as solving jigsaw puzzles; as well as reconnecting with old favorites, like reading and painting.

Q: What benefits has this balance given you?

Apoorva: Doing activities away from the keyboard makes me more productive at work. A flexible working schedule also creates a stress-free environment and allows me to bring my 100% to work. This balance helps me make time for family and friends, exercise, chores, and hobbies. Overall, having a healthy work-life balance helps me lead a fulfilling life that I am proud of.

Q: What advice would you give to a developer seeking to find a better balance?

Apoorva: The first step to finding a balance between work and life is to recognize that boundaries are healthy. I have found that putting everyday things, such as lunch breaks and walks on my work calendar is a good way to remind myself to take that break or close my laptop, while also communicating those boundaries with my colleagues. If you are having trouble doing this on your own, ask a family member, partner, or friend to remind you!

Thank you to Apoorva Joshi for sharing her insights! And thanks to all of you for reading. Look for more in our new series.

Interested in learning more about or connecting more with MongoDB? Join our MongoDB Community to meet other community members, hear about inspiring topics, and receive the latest MongoDB news and events.

And let us know if you have any questions for our future guests when it comes to building a better work-life balance as developers. Tag us on social media: @/mongodb

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Pathfinder Labs Tames Data Chaos and Unleashes AI with MongoDB

Pathfinder Labs develops software that specializes in empowering law enforcement agencies and investigators to apprehend criminals and rescue victims of child abuse. The New Zealand-headquartered company is staffed by professionals with diverse backgrounds and expertise, including counter-terrorism, online child abuse investigations, industrial espionage, digital forensics and more, spanning both the government and private sectors. Last July, I was thrilled to welcome Pathfinder Labs’ CEO Bree Atkinson, as well as co-founder and DevOps Architect, Peter Pilley to MongoDB .local Sydney where they shared more about the company’s innovative solutions powered by MongoDB. Those solutions are deployed and utilized by prestigious organizations on a global scale, including Interpol . Pathfinder Labs’ main product, Paradigm , has been built on MongoDB Atlas and runs on AWS . The tool—which relies on MongoDB’s developer data platform and document database model to sift through complex and continually growing numbers of data sets—helps collect, gather, and convert data into actionable decisions for law enforcement professionals. Pilley explained that Paradigm was “made by investigators, for investigators.” Paradigm is designed to present the information it helps gather in a way that will support a successful prosecution and outcome at trial. MongoDB Atlas enables Pathfinder Labs to tame the chaos arising from the data sets created and gathered throughout an investigation. MongoDB’s scalability and automation capabilities are particularly helpful in this regard. Powered by MongoDB Atlas, Paradigm can also easily identify similarities between cases, and uncover unique insights by bringing together information from disparate data sources. This could, for example, be about bringing together geolocalization data and metadata from an image, or identifying similar case patterns from law enforcement agencies operating in different states or countries. Ultimately, Paradigm simplifies evidence gathering and analysis, integrates external data sources and vendors, future-proof investigation methods, and helps minimize overall costs. Its capabilities are unlocking a whole new generation of data-driven investigative capabilities. During the presentation, Pilley used the example of the case of a missing female in the United States: it took a team of three investigators 12 months to solve the case. Using Paradigm, PathfinderLabs was able to solve that same case in less than an hour. “With Paradigm, we were able to feed some extra information and solve the case in 40 minutes. MongoDB Atlas allowed us to make quick decisions and present information to investigators in the most efficient way.” Pathfinder Labs also incorporates AI capabilities, including MongoDB Vector Search , which help identify which information is particularly relevant, select specific data points that can be used at a strategic point in time, connect data from one case to another, and identify what information might be missing. MongoDB Atlas Vector Search helps Pathfinder match images and details in images (i.e. people, objects), classify documents and text, and to build better search experiences for users via semantic search. “I was super excited when [Atlas Vector Search] came out. The fact that I can now have it as part of my standard workflow without having to deploy other kits all the time to support our vector searches has been an absolute game changer,” added Pilley. Finally, the team has seen great value in MongoDB’s Performance Adviser and Schema Anti Patterns features: “The performance Adviser alone has solved many problems,” concluded Pilley. To learn more and get started with MongoDB Vector Search, visit our Vector Search Quick Start page .

September 25, 2024

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MongoDB and Partners: Building the AI Future, Together

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .

November 4, 2024