Model Iterations in ML: Stages, Challenges and Best Practices

Artificial intelligence (AI) was once only used by a select few futurists and early adopters. But today, it is everywhere and has exceeded the expectations of more than 75% of executives, as per a report by Bain & Company. Moreover, 89% are using AI in some form to differentiate their products and services.

The productivity and impact of AI or Machine Learning (ML) hinges on the fundamental - the ML Model. This model is the base for how AI can use self-learning automation and data to provide incredible outcomes. For ML model refinement, the process starts not just from data collection and analytics but from how it is coded.

This blog post will explore ML model iteration, the stages, challenges, and best practices to help you build a futuristic and well-crafted AI product.

Iterative Development Stages in ML

ML models follow an approach where information is processed and reprocessed until the desired outcome is reached.

ML model iteration development happens in the following ways:

Stage 1: Data Collection and Preprocessing

ML model development stages start with data collection and preprocessing, where raw information is fed into the system for refinement. Like prospectors panning for gold, data scientists sift through vast datasets, identifying relevant nuggets and discarding the noise. This meticulous process ensures a solid foundation, laying the groundwork for a robust model.

Stage 2: Model Selection and Initial Training

Once the data is refined, the next step is to choose the appropriate model for initial training. In this stage, the algorithm takes its first steps toward learning by utilizing the curated data to establish an initial version of the model. This phase is of utmost importance and requires multiple revisions until the blueprint of the ML model is finalized.

Stage 3: Model Evaluation and Feedback

After developing a machine learning (ML) model, it is essential to evaluate it against various aspects, such as the intended objectives. The model should also be tracked against key metrics to judge its outcome and suggest improvements.

This iterative process ensures that the model is aligned with its intended purpose. It also compels you to rethink, reshape, and enhance the model's capabilities to make it more effective.

Stage 4: Refinement and Feature Engineering

After receiving feedback, the machine learning model can make further improvements. In this process, the algorithm takes feedback on the first iteration and refines itself accordingly. Once the model is refined, its output becomes more accurate, which helps train the system to understand what is expected in real-world scenarios.

Stage 5: Hyperparameter Tuning and Optimization

With the finalized version of the model, you can begin testing different metrics within a protected environment. Although this is not yet a real-world implementation, data scientists can still observe the output and take note of the results.

Subject matter experts and data analysts can then use this data to understand prediction errors and refine them until the error percentage is below the necessary threshold for use in industrial, ethical or legal frameworks.

Stage 6: Continuous Model Monitoring

Once the model is ready for real-world use, it is implemented into client-side applications. However, the ML model iterative development process doesn’t end here.

When deployed, the ML model is continuously monitored for any performance and output-related issues. This includes monitoring performance metrics, hardware and software performance, and customer-related issues.

Stage 7: Feedback Integration and Model Reiteration

After monitoring the ML model's output, feedback is actively utilized to enhance performance. This involves addressing anomalies and system performance issues. These alerts trigger immediate resolutions, contributing to ongoing model training.

These iterative refinements consistently elevate overall outcomes, ensuring heightened accuracy and effectiveness. This guarantees that the learning and improvement process remains perpetual, avoiding abrupt halts and fostering continuous evolution.

Challenges and Pitfalls

ML Model interpretability demands clarity, while test case selection is a strategic hurdle for robustness. In each stage, the idea is to ensure that the ML model is tested rigorously and uses diverse iterations to ensure that the final output has the lowest possibility of errors.

The key requirement for this is historical data. This is often a challenge since many industries have recently shifted to adopt digital technology for continuous monitoring and optimization. The lack of data and scenarios or the data being present in physical format can be an issue.

Plus, the data needs transparency, further enriching the iterative development process with diverse perspectives.

Organizations can enhance their accuracy by adopting digital processes and continuously feeding data into the ML model. This approach empowers the model to learn and improve iteratively, resulting in more accurate outcomes.

Best Practices

ML model refinement involves providing multiple iterations and use cases to the ML model, which can utilize this data to generate diverse scenarios. Rigorous test case diversity ensures robustness, providing ML models with continuous learning and diverse perspectives.

Additionally, some ML model development best practices that you must take into consideration are:

1. Transparent Decision-Making

Ensure model interpretability by making the decision-making process clear and understandable, fostering trust in your model's outcomes.

2. Diverse Test Cases

‍Fortify model robustness by rigorously testing across diverse scenarios. This ensures that your model can effectively handle real-world complexities and variations.

3. Scalable Architecture

‍Design models and systems with scalability in mind, anticipating the evolving demands of datasets and technological landscapes.

4. Clean and Documented Code

‍Maintain a solid foundation with clean, well-documented code. This not only facilitates collaboration but also streamlines the debugging and improvement processes.

5. Culture of Continuous Learning

‍Foster an environment where learning is continual—both for your models and the team behind them. Embrace new technologies, methodologies, and insights to stay at the forefront of ML innovation.

Conclusion

Ultimately, the success of any AI product boils down to two key elements:

1. The quality and diversity of the data fed into the ML model.
2. The choice of algorithm and model to effectively utilize this data.

As you keep refining your ML models, it's crucial to gather diverse and accurate data to achieve the desired outcomes. With each improvement, both machines and humans evolve, opening up endless possibilities for innovation and discovery. Keep experimenting with different algorithms and techniques to push the boundaries of what's possible with AI.

No-Code AI platforms like Markov can help provide realistic outcomes and speed up ML model iterative development and refinement, using data intelligence features like No-Code Auto-EDA, collaborative reporting, ML workflows, and more.

To know more about what Markov has to offer, book a demo today!