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As AI experts, we always recommend starting any machine learning project by framing it in a Proof of Concept (POC), to avoid wasting time and effort on a tool that might not be feasible.
Following a rigorous process, we design detailed POCs with well-defined hypotheses, their associated data-related tasks, and model candidates, key performance indicators, and risk and mitigation maps. This provides our clients with a detailed view of the roadmap to implement and validate the critical machine learning components of the solution, along with a much more representative cost estimation.
What is it?
A Proof of Concept (POC) allows AI innovators to test their ideas before implementing a full solution, enabling them to fail fast with limited spending.
Our POC design workshop follows an iterative process that maps a high-level AI idea or POC candidate to a well-defined roadmap for implementation.
To achieve this, we break down the overall goal into a series of critical hypotheses to validate. For each hypothesis, we identify the most relevant data sources for training and evaluation, and propose a set of model candidates to implement them. Additionally, we establish critical Key Performance Indicators (KPIs) coherent with your business needs, and align them with quantitative metrics and validation experiments.
This approach helps us provide accurate cost estimations for the foundational machine learning components of the overall solution, giving our clients a comprehensive blueprint of the most critical elements.
Minimize risks and ensure a cost-effective implementation of machine learning with us!
Our process
How does it go?
During our 2 week long fully-remote workshop we will follow this process with you:
1
Reviewing AI ideas
We go together through the AI opportunities you identify and analyze them from both an operational and a technical perspective. We map every single step of the processes in which the ML models will take part, to put them in context and identify potential constraints. We also map the associated high-level success criteria.
2
Hypothesis and engineering tasks mapping
With a comprehensive overview of the main goals and their context, we move towards mapping the use cases into relevant hypotheses and experiments, identifying also the engineering tasks required to make them happen. We are also aligning the hypotheses with your long-term view of the solution, to ensure that they agree with other critical functional and non-functional requirements.
3
Collaboration roadmap
We review with you the hypotheses and their main requirements to identify constraints that might jeopardize the implementation of the POC. We explore different alternatives to help you overcome those constraints and get involved in mapping them to the corresponding tasks.
4
Data diving
There's no possible ML model without the right data. To make sure we have everything we need to get things done, we map your data sources into very detailed canvases that summarize their location, size, and main characteristics. We also explore other public sources that might come in handy to complement your proprietary data.
5
KPI review and metrics association.
We decompose the high-level success criteria of the POC into the key performance indicators (KPIs) of each hypothesis. Then, our Data Science team identifies which quality metrics can be used to quantify them. Numbers never lie, so we want you to have real feedback about the true performance of the algorithms.
6
Data diving
We go through the whole POC design by checking with you all the outcomes of the process. At this point in the workshop, you have everything you need to know to validate the ideas, including the implementation costs working with our team.
What’s going on under the curtains
After each offline session, we repeat a series of iterative in-house sessions based on a proprietary workflow that includes reviews of the scientific evidence, identification of code libraries, risk assessment, and the construction of an in-depth backlog of tasks.
Model mapping.
Using research sources and well-established practices, our Data Scientists and machine learning experts explore alternative models to implement the solution.
Risk assessment.
We iterate together through the POC design to identify risks, their associated costs, and to establish potential initiatives to mitigate them.
Exploratory data analysis (EDA).
To approximate more feasible solutions, we enhance the design process with a few EDAs on your data sources
The tools that we use 🛠️
To collaborate throughout the workshop
To interact during online sessions
To prepare the final backlog
To identify suitable model candidates
To implement and deliver EDAs.
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A fully mapped set of hypotheses.
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A list of experiments, validation metrics, model candidates, and data sources.
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A complete backlog of engineering tasks and experimental duties.
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A ballpark estimate of the overall cost of the POC implementation.
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A risk register with mitigation and contingency plans.
Roles
Who will help you along the way
Our AI Labs are groups of Arionics specialized in machine learning, with experience in numerous domains, including natural language processing, predictive modeling and computer vision. For our POC design workshops, we assemble personalized groups of experts based on your domain of application and our previous experiences.
You’re gonna be helped then by a custom lab assembled exclusively for your project, with roles that include:
Product designers, who will contribute in nailing down the exact requirements and challenges of the POC.
Data Scientists, who will map broad ML tasks into appropriate minimum viable models (MVMs) and experiments.
Data Engineers, who will dive into key tasks such as data collection, extraction, label assignment, and analysis.
ML engineers, who will design the general architecture of the solution and how the MVMs will interact with one another.
This lab will collaborate in designing a detailed, doable and trustworthy POC for you to move forward in your AI adoption path.
Further resources! 🤓
Check out our blog articles about implementing POCs for AI projects.
This lab will collaborate in designing a detailed, doable and trustworthy POC for you to move forward in your AI adoption path.
What are the next steps? 🚀
Contact us and let’s get to know each other!
Get your team ready for our workshop.
Let’s go through our POC design workshop.
Implement and validate the prototypes and get ready for success!