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Buy and Build: Using a Decision Matrix to Craft a Great IoT Product
CallBox, IoT design, iot development
Building Value with IoT
Keys to Successful Machine Learning
What Do You Need to Know?
Building an ML model takes a clear understanding of the possibilities as well as risks.
But, what key elements do you need in an ML project?
What should you consider? Why or why not?
And, how do you know if you’re ready?
THE FIRST STEP
Know the Potential Benefits & Risks
Machine learning models learn, identify patterns, and make decisions with little intervention from humans leading to increased efficiency, lower costs, greater performance, and increased profitability. Building an ML model takes a clear understanding of the possibilities, potential use cases as well as risks. Our team of architects and engineers will work with you to invest in the right solutions that can scale with your company’s growth and help you achieve your business objectives.
Know What You Need
Software architecture for machine learning includes data collection, data preparation, training architecture, deployment architecture, inference architecture, and ongoing model analysis. This could include cloud deployment, edge deployment, or a hybrid strategy. Identifies the high-level machine learning design patterns, technologies, frameworks, testing methodologies, and DevOps methodologies that best fit the project use cases.
Assess Where You Are
This is a key step that shouldn't be missed. An assessment gives a high-level view of a current machine learning model(s), data collection and preparation processes, model training processes, deployment strategies, and monitoring systems. The assessment will cover characteristics surrounding security, reliability, scalability, model performance, system performance, and biases. It may also cover system modifications for cost savings, lowering risk, and improving operations.
Benefits to You
We provide you with an in-depth analysis of strengths and weaknesses with an actionable list of recommended steps, best practices, and improvements with suggested priorities. You will have a comprehensive architecture for the full ML life-cycle from data collection and training to deployment and monitoring. We'll guide you through the development and maintenance of new or existing models including bug fixes, updating code, deploying new models, and adding new features.
Attend Training
Be prepared! We offer Machine Learning Training covering business and tech topics to help your team understand ML & IoT concepts, value propositions, challenges to adopting, and creating value in Machine Learning & IoT products. Click on the "Talk to An Expert" button above to discuss training opportunities.
SpinDance has collaborated with us, jointly designing and developing our IoT platform since 2013. They bring a sense of speed and urgency to every project, and are continually focused on delivering quality software. From embedded systems through cloud, they are very knowledgeable in every aspect of IoT and skilled in every facet of software engineering, from R&D to architecture design, through developing, testing and hosting.
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