Robust and scalable Machine Learning lifecycle

Robust and scalable Machine Learning lifecycle

Robust and scalable Machine Learning lifecycle for a high performing AI team trending in 2024

There is no rejecting that we are all the way into the time of Artificial Intelligence, prodded by algorithmic, and computational advances, the accessibility of the most recent calculations in different programming libraries, cloud innovations, and the longing of organizations to release bits of knowledge from the tremendous measures of undiscovered unstructured information lying in their undertakings.


While it is clear where we are made a beeline for there is by all accounts a street blocker that I will address in this blog. Some of the time point of view is a motivation, I as of late discovered an exploration paper by Google specialists, named as Hidden Technical Debt in Machine Learning Systems. It features how little ML code is in the product (Big Picture) and how the enormous parts are regularly ignored(often because of absence of center and capabilities) prompting specialized obligation, insufficiency and frequently dissatisfaction for associations.

Robust and Scalable ML Lifecycle for a High Performing AI Team

Pic Credits: Hidden Technical Debt in Machine Learning (creators)Robust and scalable Machine Learning


Machine learning has revolutionized various industries by enabling computers to learn from data and make predictions or decisions without explicit programming. However, along with its immense potential, machine learning also introduces a concept known as “hidden technical debt.” Hidden technical debt refers to the implicit costs and challenges associated with machine learning projects that may not be immediately apparent. In this article, we explore the concept of hidden technical debt in machine learning and shed light on the challenges it presents.

Data Quality and Preprocessing:
One of the primary sources of hidden technical debt lies in the quality and preprocessing of data. Machine learning models heavily rely on high-quality, well-preprocessed data for accurate predictions. However, data collection processes may introduce biases, incomplete information, or errors that can negatively impact the performance and reliability of the models. Ensuring data quality and implementing robust preprocessing techniques is essential to uncover and address hidden technical debt at the data level.

Model Complexity and Interpretability:
As machine learning models become more sophisticated and complex, interpretability becomes a challenge. Complex models may achieve higher accuracy but lack transparency, making it difficult to understand the reasoning behind their predictions. This lack of interpretability introduces hidden technical debt by potentially hindering model debugging, compliance with regulations, and gaining user trust. Striking a balance between model complexity and interpretability is crucial to mitigate this form of hidden technical debt.

Scalability and Maintenance:
Machine learning models often require continuous updates, enhancements, and retraining as new data becomes available. Scaling and maintaining machine learning systems can be challenging, especially when dealing with large datasets or computationally intensive models. Failure to anticipate scalability and maintenance requirements introduces hidden technical debt by impeding the ability to adapt and maintain the models effectively over time.

Ethical Considerations and Bias:
Hidden technical debt can also arise from ethical considerations and bias in machine learning models. Biases present in training data or algorithmic decision-making can perpetuate unfairness or discrimination, leading to negative consequences in real-world applications. Addressing hidden technical debt related to ethical considerations requires proactive measures such as data auditing, bias detection, and algorithmic fairness techniques.

Reproducibility and Documentation:
Machine learning projects often involve multiple iterations, experiments, and variations in models and parameters. Without proper documentation and reproducibility practices, hidden technical debt can accumulate due to difficulties in reproducing previous results or understanding the rationale behind specific decisions. Establishing robust version control, documentation, and experiment tracking mechanisms is crucial for reducing hidden technical debt associated with reproducibility.

Integration and Deployment:
Integrating machine learning models into existing systems and deploying them in production environments can be complex. Hidden technical debt can accumulate if integration challenges, such as incompatible data formats or infrastructure limitations, are not adequately addressed. Additionally, monitoring model performance, handling version updates, and ensuring seamless deployment across different environments are critical to minimize hidden technical debt during integration and deployment.

Knowledge and Skill Gaps:
Hidden technical debt can also arise from knowledge and skill gaps within machine learning teams. The rapidly evolving nature of machine learning requires continuous learning and upskilling. Failure to stay updated with the latest techniques, algorithms, or best practices can result in outdated models, inefficient workflows, or missed opportunities for improvement. Investing in ongoing training and fostering a culture of knowledge sharing helps mitigate hidden technical debt associated with knowledge and skill gaps.

Hidden technical debt in machine learning represents the challenges and costs that may not be immediately apparent in projects. By addressing these challenges, organizations can minimize the accumulation of hidden technical debt and improve the efficiency, reliability, and ethical implications of machine learning systems. Recognizing the significance of data quality, interpretability, scalability, ethics, reproducibility, integration, and knowledge gaps is crucial for successfully navigating the complexities of machine learning projects and unlocking their full potential.

Normally in the creation frameworks, it so happens that it is ~20% Machine Learning and ~80% is Software Engineering code.

With customary and everyday methods of working, devices and absence of interaction driven programming advancement. It takes a ton of non-ML coding and plumbing to set up a creation prepared framework.

As increasingly more machine-learned administrations advance into programming applications, which themselves are essential for business measures, hearty life cycle the executives of these machine-learned models gets basic for guaranteeing the trustworthiness of business measures that depend on them. On top of this, According to Gartner, organizations battle to operationalize AI models:

Robust and scalable Machine Learning lifecycle

“The Gartner Data Science Team Survey of January 2018 tracked down that more than 60% of models created to operationalize them were never really operationalized.

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