The importance of monitoring machine learning models

Another type of ML model performs classifications, and precision and recall metrics can help track accuracy. Precision measures the true positives against the ones the model selected, while recall tracks a model’s sensitivity. ML monitoring can also alert on ML model drift, such as concept drift when the underlying statistics of what’s being predicted change, or data drift when the input data changes.

Don’t overlook the importance of KPIs in AI/ML projects

Challenges in data and model privacy will force organizations to embrace federated learning solutions. Last year, Google released TensorFlow Privacy, a framework that works on the principle of differential privacy and the addition of noise to obscure inputs. These machine learning models are smart enough and have a small enough memory footprint to run on smartphones and learn from the data locally. Ira Cohen, chief data scientist and cofounder at Anodot, shares important factors in ML model monitoring. “First, models should monitor output and input features’ behavior, as shifts in the input features can cause issues,” he says. He suggests using proxy measures when model performance cannot be measured directly or quickly enough.

Supply Chain Management Review

This unchecked proliferation of unauthorized AI tools introduces significant risks, from data leakage to model bias and vulnerabilities that threat actors could exploit. CISOs and IT leaders are now tasked with shining a light into the shadows– ensuring that AI-driven decisions are explainable, secure, and aligned with enterprise policies. Understanding the evolving role of MLOps in managing and securing the rapidly expanding AI/ML IT landscape is essential to safeguarding the interconnected systems that define our era. As critical measures of operational performance, KPIs are fundamental to the efficiency of supply chains.

Linking AI/ML with KPIs

Organizations will limit their use of CPUs – to solve only the most basic problems. The risk of being obsolete will render generic compute infrastructure for ML/AI unviable. Even if there are few requests, devops teams know they must upgrade apps and patch underlying components; otherwise, the software developed today will become tomorrow’s technical debt.

As he embodies continuous improvement and collaboration, Bharadwaj’s insights and contributions will undoubtedly continue influencing and inspiring future technological advancements. In addition to code, components, and infrastructure, models are built using algorithms, configuration, and training data sets. These are selected and optimized at design time but need updating as assumptions and the data change over time.

Adoption is up, panic is down: Cyber pros weigh AI’s real impact

To counter such threats, organizations need to put more emphasis on model verification to ensure robustness. Some organizations are already using adversarial networks to test deep neural networks. Similarly, we will see the emergence of a new market for model testing and model security experts, who will test, certify, and maybe take on some liability of model failure. The increase in specialized chips and hardware will also lead to incremental algorithm improvements leveraging the hardware.

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• The fusion of artificial intelligence (AI), machine learning (ML), and DevOps signifies a new era of efficiency and technological progress.
• An example of such a unit is an SKU, which may be represented in terms of how it is manufactured, which logistics services provider delivers it over the last mile or even the contracts that frame these services.
• ML monitoring can also alert on ML model drift, such as concept drift when the underlying statistics of what’s being predicted change, or data drift when the input data changes.
• Another type of ML model performs classifications, and precision and recall metrics can help track accuracy.
• Last year, Google released TensorFlow Privacy, a framework that works on the principle of differential privacy and the addition of noise to obscure inputs.

He discusses the strategic integration of these disciplines in his work, overcoming hurdles like cultural differences, ML deployment complexities, and data management. He articulates the advantages of combining AI with DevOps, particularly in healthcare technology, including operational efficiency, improved patient care, and cost reduction. It may sound like hyperbole to say that machine learning operations (MLOps) have become the backbone of our digital future, but it’s actually true. Performance indicators also anchor the complex data sets that AI/ML models analyze and interpret. This is particularly important when the objective is the digital transformation of the supply chain, projects that typically draw data from many disparate sources. Agile development teams must ensure that microservices, applications, and databases are observable, have monitoring in place to identify operational issues, and use AIops to correlate alerts into manageable incidents.

Like monitoring applications for performance, reliability, and error conditions, machine learning model monitoring provides data scientists visibility on model performance. ML monitoring is especially important when models are used for predictions or when the ML runs on datasets with high volatility. By adhering to these best practices, organizations can effectively safeguard MLOps pipelines and ensure that security measures enhance rather than impede the development and deployment of ML models. Software development largely focuses on maintaining the code, monitoring application performance, improving reliability, and responding to operational and security incidents.

Tech & Science

Artificial Intelligence (AI) and Machine Learning (ML) can reshape the way KPIs are chosen and applied and facilitate the development of new ones. Model performance management aims to address them across the development, training, deployment, and monitoring phases. When there’s low market volatility, algorithms such as the long short-term memory (LSTM) can provide rudimentary predictions, and more comprehensive deep learning algorithms can improve accuracy. But most models will struggle to make accurate predictions when markets are highly volatile, and model monitoring can alert for these conditions. Shashank Bharadwaj exemplifies how integrating AI, ML, and DevOps can change the game. His career illuminates the way for the next wave of breakthroughs in healthcare technology and beyond.

An example of such a unit is an SKU, which may be represented in terms of how it is manufactured, which logistics services provider delivers it over the last mile or even the contracts that frame these services. Because performance is measured in these different contexts, a KPI, or anchor point, ties the multiple data layers together. Artificial Intelligence  and Machine Learning (ML) are affecting many areas of supply chain management, including the use of key performance indicators (KPIs). A third concern is explainable ML, where models are stressed to determine which input features contribute most significantly to the results. This issue relates to model bias, where the training data has statistical flaws that skew the model to make erroneous predictions.