Simplifying Data Science with Iguazio and MongoDB: Modernization with Machine Learning

Gabriela Preiss
December 2, 2020 | Updated: May 6, 2021
#partners#technology#modernization

For the most innovative, forward-thinking companies, “data” has become synonymous with “big data” — and “big data” has become synonymous with “machine learning and AI.” The amount of data you have is raw knowledge. The ability to connect the dots in a cohesive picture that lets you see major projections, personalizations, security breaches, etc., in real time — that’s wisdom.

Or, as we like to call it, data science.

MongoDB Cloud is the leading cloud data platform for modern applications. Iguazio, initially inspired by the powerful Iguazu Falls in South America, is the leading data science platform built for production and real-time use cases. We’re both disrupting and leading various industries through innovation and highly forethought intelligence. It makes perfect sense for us to work together to create a powerful, data-driven solution.

Iguazio Data Science & MLOps platform optimizes data science for your use cases

Iguazio enables enterprises to develop, deploy, and manage their AI applications, drastically shortening the time required to create real business value with AI. Using Iguazio, organizations can build and run AI models at scale and in real time, deploy them anywhere (multi-cloud, on-prem or edge), and bring to life their most ambitious AI-driven strategies. Enterprises spanning a wide range of verticals use Iguazio to solve the complexities of machine learning operations (MLOps), and accelerate the machine learning workflow by automating the following, end-to-end processes:

  • Data collection — ingested from any diverse source, whether structured, unstructured, raw or real-time
  • Data preparation — through exploration and manipulation at scale (go big!)
  • Continuous model training — through acceleration and automation
  • Rapid model and API deployment
  • Monitoring and management of AI applications in production

As a serverless, cloud-native data science platform, Iguazio reduces the overhead and complexity of developing, deploying, and monitoring AI models, guarantees consistent and reproducible results, and allows mobilizing, scaling, and duplicating functions to multiple enforcement points.

MongoDB delivers unprecedented flexibility for real-time data science integration

With its scalable, adaptable data processing model, ability to build rich data pipelines, and capacity to scale out while doing both in parallel, MongoDB is a foundational persistence layer for data science. It allows you to use your data intelligently in complex analytics, drawing new conclusions and identifying actions to take. Data science and data analytics go hand-in-hand, fueled by big data.

The MongoDB data platform handles data analytics by:

  • Enabling scalability and distributed processing — processing data with a query framework that reduces data movement by knowing where that data is and optimizing in-place computation
  • Accelerating insights — delivering real-time insight and actions
  • Supporting a full data lifecycle — intelligent data tiering from ingestion to transactions to retirement
  • Leveraging a rich ecosystem of tools and machine learning for data science
How Iguazio and MongoDB partner to synthesize a seamless production environment
Here's a look at how Iguazio and MongoDB partner to synthesize a seamless production environment:

MongoDB and Iguazio: from research to production in weeks

Iguazio fuses with MongoDB to allow intelligent, complex data compilations that lead to real-world ML/AI results like streaming and analytics, IoT applications, conversational interfaces, and image recognition.

Data science is opening opportunities for businesses in all areas, from financial services to retail, marketing, telco, and IoT, and those opportunities create demands on data that continue to grow. Iguazio swiftly reduces the development and deployment of data science projects from months to weeks, transforming how businesses, developers, and product owners use and imagine new use cases for their data.

Together, MongoDB and Iguazio establish a joint hybrid/multi-cloud data science platform.

Iguazio and MongoDB data science platform

MongoDB’s unique features create the perfect seeding ground for Iguazio’s data science platform. They include:

  • MongoDB’s high-performing, highly ranked data platform experience
  • No data duplication
  • Optimization for real-time, an essential factor for data science
  • An elastic, flexible model that adjusts to ever-changing load requirements
  • Production that’s ready in minutes

Meanwhile, Iguazio’s powerful ML pipeline automation simplifies the complex data science layer by creating a production-ready environment with an end-to-end MLOps solution, including:

  • A feature store for managing features (online and offline) that resides in MongoDB
  • Data exploration and training at scale, using built-in distribution engines such as Dask and Horovod
  • Real-time feature engineering using Iguazio’s Nuclio-supported serverless functions
  • Model management and model monitoring, including drift detection
  • Open and integrated Python environment, including built-in libraries and Jupyter Notebook-as-a-Service

Data and data science in the real world

When we think of data, stagnant databases may come to mind. But data in action is live, quick, and moves in real time. Data science is no different — and it has quickly incorporated itself in every sector of virtually every industry:

  • Fraud prevention — distinguishing legitimate from fraudulent behavior and learning to prevent new tactics over time
  • Predictive maintenance — finding patterns to predict and prevent failures
  • Real-time recommendation engines — processing consumer data for immediate feedback
  • Process optimization — minimizing costs and improving processes and targets
  • Remote monitoring — quickly detecting anomalies, threats, or failures
  • Autonomous vehicles — continuously learning new processes and landscapes to optimize safety, performance, and maintenance
  • Smart scheduling — increasing coordination among nearly infinite variables
  • Smart mobility systems — using predictive optimization to maintain efficiency, safety, and accuracy
  • IoT & IIoT — generating insights to identify patterns and predict behavior

Data science today

MongoDB enables a more intuitive process for data management and exploration by simplifying and enriching data. Iguazio helps turn data into smarter insights by simplifying organizations’ modernization into machine learning, AI, and the ongoing spectrum of data science — and we’ve only just scratched the surface. To learn more about how, together, Iguazio and MongoDB can transform your data processes into intelligent data science, check out our joint webinar discussing multiple client use cases.

MongoDB and modernization

To learn more about MongoDB’s overall modernization strategy for moving from legacy RDBMS to MongoDB Atlas, read here.

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How the Austin Chapter of MongoDB’s Women’s Group Built Community During the Pandemic

MongoDB is on a mission to create an inclusive workplace where every single employee can thrive. With a range of established affinity groups — and new ones forming regularly — MongoDB looks for ways to amplify those groups’ efforts and help support their overall mission. When the COVID-19 pandemic forced offices to shut down and employees to work from home, our affinity groups were challenged to find creative ways to support and grow their now-remote communities. As leaders of the MongoDB Women’s Group Austin chapter, we share how we pivoted this challenge into an opportunity. First, What's the MongoDB Women's Group The MongoDB Women’s Group is a community of MongoDB employees identifying as women, nonbinary, or trans. Our mission is to create a bold, visible, and united force for gender equality. To help us get there, the MongoDB Women’s Group hosts monthly members-only meetings as well as events open to both members and allies. Relaunched in 2018, the Austin-based chapter connects women and allies in our Austin office to a community of local companies and women’s groups that can support their growth within the tech industry. Pre-COVID, we gained a lot of momentum with our events, which included a live speaker series in the office, yoga, and events focused on subjects such as fertility and imposter syndrome. When COVID-19 hit, we faced a new challenge: how do we create a sense of community for our members when everyone works completely remote? Although initially daunting, the challenge of organizing remote events was an opportunity in disguise. It enabled us to kick off a speaker series for all employees, featuring prominent women in leadership positions across the country. Enter Angie Brown, from The Home Depot. Angie was the first woman to join our remote speaker series, and we couldn’t have asked for a better person to kick it off. She began her career at The Home Depot in 1998 as an entry-level software developer and now is Vice President of Technology — Merchandising, leading a team that develops solutions to support cataloging, pricing, and assortment capabilities at the giant retail chain. She also helps to mentor aspiring leaders in a number of ways, including actively participating in Atlanta’s Women in Technology association. Here, we share some highlights from our fireside chat with Angie during which she discussed her career and provided advice on what women can do to set themselves up for success. Fireside Chat with Angie Brown MongoDB: What advice do you have for those just starting off in their careers? Angie Brown: Opportunities can look like problems and not everyone wants to run into the fire, but avoiding problems can really be a missed opportunity. That’s one important lesson I’ve learned throughout my career. Although you should have a general idea of where you want to go, you also need to be willing to flex. Things might unfold in ways you didn’t expect. If you’re too prescriptive, you might miss out on them. So, you need to find a way to strike a balance. MongoDB: You took a role in leadership fairly early. How did you change your skills and evolve as you moved up? AB: When I talk to people considering moving into management, I ask them to look at the job and determine if the required qualities and responsibilities would make them happy. It’s not just about the title and pay increase. When you pivot from being an individual contributor to being in a leadership role, servant leadership is a huge part of it. If you look at management as a way to control, you won’t be happy. If you look at it as a way to serve others and help them be successful, then you’ll find joy in that career shift. I didn’t prethink this when I first moved into management and had a little bit of an identity crisis. I was used to being the one who got things done. All of a sudden, my role and life was all about going to meetings, and I didn’t look at meetings as tangible work. I was over it. Where was the joy in this? If your joy comes from having your hands on the keyboard and needing to do things your way, then being in management would be like fitting a square peg in a round hole. At first I felt invalidated and unsure of myself because it wasn’t my hands on the keyboard. I had to work through that and do a little soul-searching. I reframed my thinking to be happy leading a team and helping them solve their problems, even if it meant I wasn’t solving them myself. I had a lightbulb moment when I moved into a director role when I realized I was still solving big problems by helping my team tackle them. There’s nothing wrong with where you find your joy and no judgement if your passion aligns as an individual contributor; we need amazing developers! Always be aware of the work itself and make sure it aligns with what you enjoy. MongoDB: How have mentors played a role in your success? AB: I wish I had invested in mentors much sooner. In the early stages of my career, I didn’t think I needed help and believed I could just figure it all out on my own. I thought asking for help was a sign of weakness. In hindsight, my mentors have absolutely formed part of who I am today. I don’t have just one mentor. Instead, I look at a topic and focus on finding a mentor for that specific topic. With that approach, I have ended up having a number of mentors. Thank you again to Angie Brown! We appreciate your insight and inspiration. If you are interested in joining MongoDB, explore our career opportunities and join an innovative team that is disrupting the database industry every day.

November 30, 2020
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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024