The text outlines the challenges faced by industries without real-time forecasts and introduces the integration of MongoDB’s time series data management capabilities with Amazon SageMaker Canvas for overcoming these challenges. It details the solution architecture, prerequisites, and step-by-step processes for setting up the solution using MongoDB Atlas and Amazon SageMaker Canvas. The post concludes with resources and information about the authors.
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Real-Time Forecasting Solutions for Businesses
As industries evolve in today’s fast-paced business landscape, the inability to have real-time forecasts poses significant challenges for industries heavily reliant on accurate and timely insights. The absence of real-time forecasts in various industries presents pressing business challenges that can significantly impact decision-making and operational efficiency. Without real-time insights, businesses struggle to adapt to dynamic market conditions, accurately anticipate customer demand, optimize inventory levels, and make proactive strategic decisions.
Challenges Faced by Industries
Industries such as Finance, Retail, Supply Chain Management, and Logistics face the risk of missed opportunities, increased costs, inefficient resource allocation, and the inability to meet customer expectations.
Solution Overview
By harnessing the transformative potential of MongoDB’s native time series data capabilities and integrating it with the power of Amazon SageMaker Canvas, organizations can overcome these challenges and unlock new levels of agility.
MongoDB Atlas
MongoDB Atlas is a fully managed developer data platform that simplifies the deployment and scaling of MongoDB databases in the cloud. It provides a fully managed database with built-in full-text and vector search, support for geospatial queries, charts, and native support for efficient time series storage and querying capabilities.
Amazon SageMaker Canvas
Amazon SageMaker Canvas is a visual machine learning (ML) service that enables business analysts and data scientists to build and deploy custom ML models without requiring any ML experience or having to write a single line of code. It supports a number of use cases, including time-series forecasting.
Practical Implementation Steps
- Persist transactional time series data in MongoDB Atlas.
- Extract data into Amazon S3 bucket through Atlas Data Federation.
- Access the data in Amazon SageMaker Canvas to build models and create forecasts.
- Visualize forecast data in MongoDB Atlas Charts.
Conclusion
By leveraging MongoDB’s time series data and Amazon SageMaker Canvas, organizations can accelerate time-to-insight, make informed decisions, and stay competitive in today’s fast-paced business environment.
Resources
- Try out MongoDB Atlas
- Try out MongoDB Atlas Time Series
- Try out Amazon SageMaker Canvas
- Try out MongoDB Charts
About the Authors
Igor Alekseev is a Senior Partner Solution Architect at AWS in Data and Analytics domain. Babu Srinivasan is a Senior Partner Solutions Architect at MongoDB.
AI Solutions and Contact Information
Discover how AI can redefine your way of work. For AI KPI management advice, connect with us at hello@itinai.com. Explore practical AI solutions at itinai.com/aisalesbot.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas
- AWS Machine Learning Blog
- Twitter – @itinaicom
