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Managed ServiceDrive Innovation With Machine Learning
Accelerate Your Digital Transformation With Machine Learning On AWS
Belle Fleur and AWS make machine learning accessible to all
Put machine learning at the heart of your business to fuel innovation and create new capabilities. Our solution along with the AWS centralized approach to machine learning makes your business more intelligent.
Learn how our machine learning solution with AWS can help accelerate digital transformation for your teams.
Come With an Idea, Leave With a Solution
The Data Flywheel Lab program has two offerings - the Build Lab and the Design Lab. All AWS Data Flywheel Lab engagements are hosted either online. In the AWS Data Flywheel Lab, your team will be hyper-focused on the pre-defined use case that you selected for the lab. During the lab, AWS Data Flywheel Lab Solutions Architects and AWS service experts support the customer by providing prescriptive architectural guidance, sharing best practices, and removing technical roadblocks. Customers leave the engagement with an architecture or working prototype that is custom fit to their needs, a path to production, deeper knowledge of AWS Databases, Analytics, and Machine Learning services, and new relationships with AWS service experts.
Think Big
At the Design stage, the focus is on bridging the gap between innovation and technology. Leading up to your Lab engagement, you will have a series of calls with our AWS Solutions Architect to walk through your business use case and understand your goals.
The Design Lab is a one-half to two day engagement for customers who need a real-world architecture recommendation based on AWS expertise, but are not yet ready to build. In a Design Lab, your team will spend one-half to two days in a non-build exercise, discussing architecture pattern and anti-pattern designs for your specific use case, best practices for building, and recommended strategies for design and delivery. Your team will leave with a document reflecting the Data Flywheel Lab's recommendations for your design approach and architecture.
Start Small
In the AWS Build Lab, your team will be hyper-focused on the pre-defined use case that you selected for the lab.
The Build Lab is a two to five day intensive build with a technical customer team. In a Build Lab, your team will spend two to five days building hands-on with your data in your own AWS account with the guidance of AWS service experts and your dedicated AWS Data Flywheel Lab Solutions Architect. Your days will consist of: build, test, review progress, repeat! On the last day of your lab, your team will leave with a validated architecture and working prototype to use as a guide for your production deployment.
Scale Fast
The Execute Scale Plan focuses on bold technology ideas that deliver game-changing results for our customers. Our multi-disciplined AWS Solutions Architects ensure that proposed solutions solve business problems and are designed with the future in mind.
By engaging with the AWS Build Lab, you'll be able to accelerate your projects by an average of two months. Customers attribute this acceleration to the enablement of the AWS Design & Build Lab to help them make architectural and operational decisions faster, remain hyper-focused on a single project over a series of days, and learn new skills first-hand from AWS experts. Following up with multiple AWS Design & Build Labs until your project is successfully implemented.
IDEATION
Prepare: Support For Well-Architected Review, Business Use Case And Persona Selection
Design: Guidance Designing A Solution Architecture, Readout Document And Slide Summary
Think Big Roadmap: With A Storyboard For Business & It Stakeholder Use Case Alignment, Defined Business Outcomes, PR/FAQ
Delivery Plan: High Level Architecture For First Wave Of A Scale Plan(60-Day) MVP Build
Lab Length: 1/2 - 2-Day Workshop, With A Standard Length Of 2 Days To Scope Customize Solutions And The Minimum Viable Product Definition (MVP), Data Flywheel Immersion Day
Deliverable: Document Outlining Design Recommendations And Architecture
Post-Lab Next Step: Builds & Deploy Prototype To Production (Either With Belle Fleur’s Acceleration Build Lab Or Customer On Their Own)
ACCELERATION
Design Lab: Guidance, Designing & Architecture Based On Use Case Prioritization
Prototype Build: Accelerators & Programs For Building A Joint Functioning Prototype With Customer, AWS & Belle Fleur
AWS Quick Starts & Solutions Deployment: Seek To Minimize Time, Resources, And Costs
Lab Length: 3 - 8 Normalize Story Points, With A Standard Length Of 5 Developer Days To Customize Solutions
Deliverable: Working Prototype And Accelerated Next Steps For Scale Production
Scale Plan: Delivery Plan For 8 Waves Of Build Labs - 60-Day MVP Build
Post-Lab Next Step: Build & Deploys MVP Scale Plan Solution To Production (Either With Belle Fleur’s ProServe Innovation Scale Lab Or Customer On Their Own)
INNOVATION
Build Lab: 8 Waves To Execute Scale Plan - Platform MVP Build, Migrate & Build Data Flywheel Products
Assess Lab: Scale Readiness Assessment
Prioritize Lab: Business Priorities, Use Cases, Roadmap & Strategy and Benefits
Organize Lab: Data Community, Data Literacy and Training Programs, Templates and Tooling
Secure Lab: Integrated Security
Operate Lab: Automation, Model Deployment, Metrics and Monitoring
Ongoing Maturity Assessments: To Gauge Enterprise Transformation, Data Culture, Embedded Innovation, Skill Set Development
Incremental Scale Business Value Outcome: Data, People and Platform According To Prioritized Business Use Cases Roadmap
Why choose Belle Fleur and AWS for Machine Learning?
Belle Fleur and AWS offer the resources and expertise you need to establish a centralized approach to machine learning that makes innovation spread across teams. Whether you choose to make your applications more intelligent, analyze data on a deeper level, or forecast outcomes, AWS helps make machine learning accessible.
Access ML Services
AWS drives innovation through AI/ML. When you deploy your data warehouse on AWS, you can store all of your data in open formats without needing to move or transform it, giving you a future-ready platform that allows you flexibility in analyzing your data.Lower your costs
With AWS-powered solutions, you can analyze petabytes of data quickly and cost efficiently, giving you higher performance and more scalability..Start right away
Confidently run mission-critical workloads, even in highly regulated industries, as AWS enables security and compliance, and automates time-consuming administration tasks.Drive innovation
Innovate faster with a broad set of AWS services and partner. Scale with on-demand computing, pay as you go
BFT Sumadi Case Study 12-15-23- Fixed.pdfAI Solutions & ML Case Studies
Build virtually any type of application or backend service using a AI/ML services.
Rekognition Custom Labels
With Amazon Rekognition Custom Labels, Belle Fleur and AWS take care of the heavy lifting for you. Builds off of Rekognition’s existing capabilities, which are already trained on tens of millions of images across many categories. Instead of thousands of images, you simply need to upload a small set of training images (typically a few hundred images or less) that are specific to your use case into our easy-to-use console.
SIMPLIFY DATA LABELING & FEEDBACK
The Rekognition Custom Labels console provides a visual interface to make labeling your images fast and simple. The interface allows you to apply a label to the entire image or to identify and label specific objects in images using bounding boxes with a simple click-and-drag interface.
AUTOMATED MACHINE LEARNING
No machine learning expertise is required to build your custom model. Rekognition Custom Labels includes AutoML capabilities that take care of the machine learning for you. Once the training images are provided, Rekognition Custom Labels can automatically load and inspect the data, select the right machine learning algorithms, train a model, and provide model performance metrics.
SIMPLIFIED MODEL EVALUATION & INFERENCE
Evaluate your custom model’s performance on your test set. For every image in the test set, you can see the side by side comparison of the model’s prediction vs. the label assigned. You can also review detailed performance metrics such as precision/recall metrics, f-score, and confidence scores. You can start using your model immediately for image analysis, or iterate and re-train new versions with more images to improve performance. After you start using your model, you track your predictions, correct any mistakes and use the feedback data to retrain new model versions and improve performance.
Get Insights With A Modern Machine Learning Solution
Learn how AWS and Belle Fleur create solutions that ignite innovative ML opportunities across your business..