Over the past decade, I‘ve witnessed firsthand how artificial intelligence has gone from buzzword to business transformation driver across industries. As an expert in data extraction and analytics, I‘ve worked with numerous companies to help develop and deploy impactful AI systems.
Based on these experiences, I‘ve outlined below the 7 critical steps organizations should follow to develop robust AI systems in 2024.
1. Define Goals and Scope
The first step is to clearly define the goals and scope of your AI system.
As an expert data consultant, I always advise clients to start by identifying the specific business challenges or pain points the AI system aims to address. Get input from stakeholders across business units – like customer service, operations, marketing – to understand different needs.
Specifying clear objectives and user requirements early on provides a north star to guide the development process. It also helps secure executive buy-in by demonstrating the AI system‘s business value.
For example, a retailer looking to improve customer retention could set goals for their AI system like:
- Provide personalized product recommendations
- Predict customers at high risk of churn
- Identify the root causes driving churn
With well-defined goals, you can determine the appropriate scope and capabilities required to meet them. This prevents scope creep down the line.
I recommend creating a requirements document that captures:
- Use cases
- Functional requirements
- Data needs
- Performance KPIs
- Customer personas and journeys
A detailed requirements document aligns stakeholders and sets your AI initiative up for success.
2. Assemble the Right Team
AI system development requires a diverse mix of skill sets. Based on my experience, these are among the key roles needed:
- Business analysts: Understand use cases and translate needs into technical requirements.
- Data scientists: Build, test, and validate AI models.
- Data engineers: Collect, clean, label, and prepare data for training AI models.
- Machine learning engineers: Develop and optimize machine learning pipelines.
- Software engineers: Architect and code the system infrastructure and applications.
- Project managers: Coordinate across disciplines and oversee timelines.
For enterprises new to AI, gaps in specialized skills like machine learning are common. In these cases, I advise augmenting internal teams with experienced AI consultancies or freelancers.
The right blend of domain expertise, technical skills, and tools is crucial for developing effective AI systems. Assembling this cross-functional team early helps execute quickly.
3. Curate High-Quality Training Data
"Garbage in, garbage out" is perhaps the biggest pitfall in AI development. Without clean, relevant training data in sufficient volumes, even the most advanced machine learning algorithms will fail.
From my experience, here are key aspects of curating high-quality training data:
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Understand data challenges upfront: Inventory your organization‘s internal data sources and objectively evaluate their quality, coverage, and biases through statistical analysis. Identify gaps that need filling with external data.
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Map data to use cases: Determine which datasets best represent the problem space for your AI system‘s intended uses. For a predictive maintenance scenario, this could include historical sensor data from equipment.
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Clean and preprocess: Training data often needs significant cleansing and preprocessing – like handling missing values or removing anomalies – before it can be fed into models. Build data pipelines to automate this.
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Annotate unstructured data: Use techniques like sentiment labelling on text data or object bounding boxes on images to extract knowledge from unstructured data. Leverage data annotation services as needed.
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Ongoing monitoring: Continuously monitor training data quality as new data is captured over time. Shift left on data validation to catch issues early.
High-quality training data is the fuel for developing accurate and robust AI systems. Allocating sufficient time and resources for this step pays dividends later.
4. Select the Right AI Models
With the rise of pretrained models and automated machine learning, the barrier to building AI systems has lowered. But simply throwing models at a problem is unlikely to work.
Based on the defined use cases and data types, you have to carefully select the right AI models to apply. Here are key considerations I advise clients on:
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The task: Is it classification, object detection, speech recognition, forecasting etc? Different models excel at different tasks.
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Data modality: Is the data in the form of tables, images, text or a mix? Certain models work better for certain modalities.
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Speed and accuracy requirements: For real-time applications, prioritize speed without compromising too much on accuracy.
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Explainability needs: In regulated environments like healthcare, prefer transparent models.
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Infrastructure: Will you train models on cloud platforms or on-premise environments? This impacts tools used.
Often, developing custom models from scratch is unnecessary. I recommend starting with pretrained models – like TensorFlow for image models or HuggingFace for NLP – which can be fine-tuned on your data. This requires far less training data and compute resources while retaining high accuracy.
Be rigorous in benchmarking different models and tuning hyperparameters – don‘t go by gut feel alone. Follow an empirical approach.
5. Build a Reliable ML Pipeline
In my experience, one of the biggest challenges in developing enterprise-grade AI systems is piecing together the many moving parts – from data ingestion to model deployment – into a coherent machine learning pipeline.
This requires:
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Automated data pipelines: to extract, move and transform training data from source to destination in a repeatable flow.
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Model training environments: that allow iterative development and testing – on cloud platforms like GCP or AWS or on internal infrastructure.
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Workflow orchestration: to sequence pipeline stages, conditioning outputs of one stage as inputs to the next.
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Deployment automation: to publish trained models from development to production environments in a standardized way.
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Monitoring and testing: with log tracking, integration testing and automated alerting on issues.
To smooth the process, I highly recommend leveraging MLOps tools like Kubeflow Pipelines to package the machine learning workflow in a reusable way. Always architect your pipeline for scale from the start.
Specialized MLOps engineers are invaluable for building robust pipelines to move models to production efficiently.
6. Test and Validate Extensively
Rigorously testing and validating the AI system prior to launch is crucial to avoid incidents down the line. I guide clients through testing focused on these aspects:
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Unit testing: to verify the output of each component in the ML pipeline.
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Integration testing: to evaluate end-to-end system performance across different use cases.
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Edge case testing: with data that has anomalies, missing inputs etc. to ensure graceful failure handling.
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User acceptance testing: with target user groups across relevant journeys to get feedback.
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Bias testing: to detect racial, gender or other biases inadvertently captured in the training data.
I also recommend setting up CI/CD pipelines to run automated model tests and validation checks with each code change. This accelerates finding issues early.
Extensive testing provides confidence that your AI system will perform reliably when deployed. Allow sufficient test cycles during development.
7. Plan Maintenance from Day One
Unlike traditional software applications, AI systems require special maintenance due to a phenomenon called model drift.
As new data flows in, it gradually invalidates the patterns learned by the model during initial training. This causes accuracy to deteriorate over time.
To prevent this, organizations must plan their AI maintenance strategy upfront:
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Monitor key performance metrics continuously and establish alerting thresholds.
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Re-evaluate models periodically and retrain where accuracy drops below acceptable levels.
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Architect models from the start for modularity, such that individual components can be swapped in/out easily.
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Implement strong data governance and model versioning to ensure consistency across re-trains.
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Assign ownership for maintenance within the team and budget time for it.
With the right maintenance strategy, you can extend the lifetime and value realized from AI investments.
Moving Forward
Developing an AI system is a complex undertaking requiring coordination across business, data and technology units. With the proliferation of tools and cloud platforms in recent years, the barrier to building AI has lowered significantly.
However, execution challenges remain. By following a structured process – validated by industry leaders and experts – AI teams can overcome key hurdles in developing production-grade systems.
Equally important is iterating continuously even after deployment. Meeting regularly with business stakeholders and monitoring performance metrics helps gauge opportunities for improvement.
With the right strategy, developing and continuously enhancing AI is within reach for forward-looking enterprises. The time for deploying AI across the business is now.