A Smarter Factory Floor with MongoDB Atlas and Google Cloud's Manufacturing Data Engine

The manufacturing industry is undergoing a transformative shift from traditional to digital, propelled by data-driven insights, intelligent automation, and artificial intelligence. Traditional methods of data collection and analysis are no longer sufficient to keep pace with the demands of today's competitive landscape. This is precisely where Google Cloud’s Manufacturing Data Engine (MDE) and MongoDB Atlas come into play, offering a powerful combination for optimizing your factory floor.

Unlock the power of your factory data

MDE is positioned to transform the factory floor with the power of cloud-driven insights. MDE simplifies communication with your factory floor, regardless of the diverse protocols your machines might use. It effortlessly connects legacy equipment with modern systems, ensuring a comprehensive data stream. MDE doesn't just collect data, it transforms it. By intelligently processing and contextualizing the information, you gain a clearer picture of your production processes in real-time with a historical pretext. It offers pre-built analytics and AI tools directly addressing common manufacturing pain points. This means you can start finding solutions faster, whether it's identifying bottlenecks, reducing downtime, or optimizing resource utilization. Conveniently, it also offers great support for integrations that can further enhance the potential of the data (e.g. additional data sinks).

The MongoDB Atlas developer data platform enhances MDE by providing scalability and flexibility through automated scaling to adapt to evolving data requirements. This makes it particularly suitable for dynamic manufacturing environments. MongoDB’s document model can handle diverse data types and structures effortlessly while being incredibly flexible because of its native JSON format. This allows for enriching MDE data with other relevant data, such as supply chain logistics, for a deeper understanding of the factory business. You can gain immediate insights into your operations through real-time analytics, enabling informed decision-making based on up-to-date data. While MDE offers a robust solution for collecting, contextualizing, and managing industrial data, leveraging it alongside MongoDB Atlas offers tremendous advantages

Inside the MDE integration

Google Cloud’s Manufacturing Data Engine (MDE) acts as a central hub for your factory data. It not only processes and enriches the data with context, but also offers flexible storage options like BigQuery and Cloud Storage. Now, customers already using MongoDB Atlas can skip the hassle of application re-integration and make this data readily accessible for applications. Through this joint solution developed by Google Cloud and MongoDB, you can seamlessly move the processed streaming data from MDE to MongoDB Atlas using Dataflow jobs. MDE publishes the data via a Pub/Sub subscription, which is then picked up by a custom Dataflow job built by MongoDB. This job transforms the data into the desired format and writes it to your MongoDB Atlas cluster. Google Cloud’s MDE and MongoDB Atlas utilize compatible data structures, simplifying data integration through a shared semantic configuration. Once the data resides in MongoDB Atlas, your existing applications can access it seamlessly without any code modifications, saving you time and effort. The flexibility of MDE, combined with the scalability and ease of use of MongoDB Atlas, makes this a powerful and versatile solution for various data-driven use cases such as predictive maintenance and quality control, while still providing factory ownership of the data. Instructions on how to set up the dataflow job are available in the MongoDB github repository.

Conclusion

If you want to level up your manufacturing data analytics, pairing MDE with MongoDB Atlas provides a proven, easy-to-implement solution. It's easy to get started with MDE and MongoDB Atlas.

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Unleashing Developer Potential–and Managing Costs–with MongoDB Atlas

In today's business landscape, where unpredictability has become the norm, engineering leaders have to balance the dual challenges of managing uncertainty while optimizing IT costs. Indeed, the 2024 MarginPLUS Deloitte survey—which draws on insights from over 300 business leaders—emphasizes a collective pivot towards growth initiatives and cost transformations amidst the fluctuating global economic climate. MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More MongoDB Atlas: A developer's ally for cost-effective productivity Executives across industries want to cut costs without impacting innovation; based on the Deloitte survey , 83% of companies are looking to change how they run their business margin improvement efforts. This is where MongoDB Atlas , the most advanced cloud database service on the market, comes in. An integrated suite of data services that simplify how developers build with data, MongoDB Atlas helps teams enhance their productivity without compromising on cost efficiency by offering visibility and control over spending—balancing developer freedom with data governance and cost management. This helps organizations escape the modernization hamster wheel—or the vicious cycle of continuously updating technology without making real progress, and draining resources while failing to deliver meaningful improvements. Put another way, MongoDB gives teams more time to innovate, instead of just maintaining the status quo. Outlined below are the built-in features of MongoDB Atlas, which enable customers to get the most out of their data while also focusing on budget optimization. Strategic features for cost optimization with MongoDB Atlas Right-sizing your cluster Use MongoDB Atlas’s Cluster Sizing Guide or auto-scalability to match your cluster with your workload, optimizing resource use with options for every requirement, including Low CPU options for lighter workloads. Pausing clusters and global distribution Save costs by pausing your cluster , and securely storing data for up to 30 days with auto-resume. Furthermore, Global Clusters improve performance across regions while maintaining cost efficiency and compliance. Index and storage management Enhance performance and reduce costs with MongoDB Atlas’s Performance Advisor , which provides tailored index and schema optimizations for better query execution and potential reductions in cluster size. Strategic data management Reduce storage expenses using Online Archive for infrequently accessed data and TTL indexes for efficient Time Series data management, ensuring only essential data is stored. Securely backup data before deletion with mongodump. Enhanced spend management Use spend analysis, billing alerts , and usage insights via the Billing Cost Explorer for detailed financial management and optimization. Resource tagging and customizable dashboards provide in-depth financial reporting and visual expense tracking, supporting effective budgeting and cost optimization. Additionally, Opt for serverless instances to adjust to your workload's scale, offering a pay-for-what-you-use model that eliminates overprovisioning concerns. Transforming uncertainty into advancement MongoDB Atlas equips IT decision-makers and developers with the features and tools to balance developer productivity with strategic cost management, transforming economic uncertainty into a platform for strategic advancement. MongoDB Atlas is more than a database management solution; it’s a strategic partner in optimizing your IT spending, ensuring that your organization remains agile, efficient, and cost-effective in the face of change. Need expert assistance in taking control of your MongoDB Atlas costs? MongoDB’s Professional Services team can provide a deep-dive assessment of your environment to build a tailored optimization plan—and to help you execute. Reach out to learn how we can support your cost optimization goals! If you haven't yet set up your free cluster on MongoDB Atlas , now is a great time to do so. You have all the instructions in this DevCenter article .

April 8, 2024
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Building AI with MongoDB: Conversation Intelligence with Observe.AI

What's really happening in your business? The answer to that question lies in the millions of interactions between your customers and your brand. If you could listen in on every one of them, you'd know exactly what was up--and down. You’d also be able to continuously improve customer service by coaching agents when needed. However, the reality is that most companies have visibility in only 2% of their customer interactions. Observe.AI is here to change that. The company is focused on being the fastest way to boost contact center performance with live conversation intelligence. Founded in 2017 and headquartered in California, Observe.AI has raised over $200m in funding. Its team of 250+ members serves more than 300 organizations across various industries. Leading companies like Accolade, Pearson, Public Storage, and 2U partner with Observe.AI to accelerate outcomes from the frontline to the rest of the business. The company has pioneered a 40 billion-parameter contact center large language model (LLM) and one of the industry’s most accurate Generative AI engines. Through these innovations, Observe.AI provides analysis and coaching to maximize the performance of its customers’ front-line support and sales teams. We sat down with Jithendra Vepa, Ph.D, Chief Scientist & India General Manager at Observe.AI to learn more about the AI stack powering the industry-first contact center LLM. Can you start by describing the AI/ML techniques, algorithms, or models you are using? “Our products employ a versatile range of AI and ML techniques, covering various domains. Within natural language processing (NLP), we rely on advanced algorithms and models such as transformers, including the likes of transformer-based in-house LLMs, for text classification, intent and entity recognition tasks, summarization, question-answering, and more. We embrace supervised, semi-supervised, and self-supervised learning approaches to enhance our models' accuracy and adaptability." "Additionally, our application extends its reach into speech processing, where we leverage state-of-the-art methods for tasks like automatic speech recognition and sentiment analysis. To ensure our language capabilities remain at the forefront, we integrate the latest Large Language Models (LLMs), ensuring that our application benefits from cutting-edge natural language understanding and generation capabilities. Our models are trained using contact center data to make them domain-specific and more accurate than generic models out there.” Can you share more on how you train and tune your models? “In the realm of model development and training, we leverage prominent frameworks like TensorFlow and PyTorch. These frameworks empower us to craft, fine-tune, and train intricate models, enabling us to continually improve their accuracy and efficiency." "In our natural language processing (NLP) tasks, prompt engineering and meticulous fine-tuning hold pivotal roles. We utilize advanced techniques like transfer learning and gradient-based optimization to craft specialized NLP models tailored to the nuances of our tasks." How do you operationalize and monitor these models? "To streamline our machine learning operations (MLOps) and ensure seamless scalability, we have incorporated essential tools such as Docker and Kubernetes. These facilitate efficient containerization and orchestration, enabling us to deploy, manage, and scale our models with ease, regardless of the complexity of our workloads." "To maintain a vigilant eye on the performance of our models in real-time, we have implemented robust monitoring and logging to continuously collect and analyze data on model performance, enabling us to detect anomalies, address issues promptly, and make data-driven decisions to enhance our application's overall efficiency and reliability.” The role of MongoDB in Observe.AI technology stack The MongoDB developer data platform gives the company’s developers and data scientists a unified solution to build smarter AI applications. Describing how they use MongoDB, Jithendra says “OBSERVE.AI processes and runs models on millions of support touchpoints daily to generate insights for our customers. Most of this rich, unstructured data is stored in MongoDB. We chose to build on MongoDB because it enables us to quickly innovate, scale to handle large and unpredictable workloads, and meet the security requirements of our largest enterprise customers.” Getting started Thanks so much to Jithendra for sharing details on the technology stack powering Observe.AI’s conversation intelligence and MongoDB’s role. To learn more about how MongoDB can help you build AI-enriched applications, take a look at the MongoDB for Artificial Intelligence page. Here, you will find tutorials, documentation, and whitepapers that will accelerate your journey to intelligent apps.

April 29, 2024