Shashank Bharadwaj on the convergence of ML, AI, and DevOps: A tech leader’s perspective

According to a Gartner survey, 48% of global CIOs will deploy AI by the end of 2020. However, despite all the optimism around AI and ML, I continue to be a little skeptical. In the near future, I don’t foresee any real inventions that will lead to seismic shifts in productivity and the standard of living. Businesses waiting for major disruption in the AI/ML landscape will miss the smaller developments. Software is an omnipresent component of our day-to-day lives, operating quietly but indispensably behind the scenes. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.

AI/ML coupled with performance indicators can be a powerful combination when the goal is to improve a supply chain process or achieve across-the-board efficiencies. Organizations aspiring to drive value through their AI investments need to revisit the implications on their data pipelines. The trends I’ve outlined above underscore the need for organizations to implement strong governance around their AI/ML solutions in production. It’s too risky to assume your AI/ML models are robust, especially when they’re left to the mercy of platform providers. Therefore, the need of the hour is to have in-house experts who understand why models work or don’t work. These layers of data represent different units and entities and must be connected end-to-end if the project is to achieve its goals.

Business

When users and business stakeholders want enhancements, many devops teams follow agile methodologies to process feedback and deploy new versions. The fusion of artificial intelligence (AI), machine learning (ML), and DevOps signifies a new era of efficiency and technological progress. With a strong engineering background, Bharadwaj has established himself at the forefront of AI, ML, and DevOps, particularly in the healthcare industry. From leading major projects at Intuitive to his instrumental role in integrating AI with DevOps, his work combines technical expertise and visionary leadership. I also foresee a gradual shift in the focus on data privacy towards privacy implications on ML models. A lot of emphasis has been placed on how and what data we gather and how we use it.

Tech & Science

• He suggests using proxy measures when model performance cannot be measured directly or quickly enough.
• Artificial Intelligence (AI) and Machine Learning (ML) can reshape the way KPIs are chosen and applied and facilitate the development of new ones.
• Businesses waiting for major disruption in the AI/ML landscape will miss the smaller developments.
• Model performance management aims to address them across the development, training, deployment, and monitoring phases.

Recent examples of algorithm improvements include Sideways to speed up DL training by parallelizing the training steps, and Reformer to optimize the use of memory and compute power. With ML solutions becoming more demanding in nature, the number of CPUs and RAM are no longer the only way to speed up or scale. More algorithms are being optimized for specific hardware than ever before – be it GPUs, TPUs, or “Wafer Scale Engines.” This shift towards more specialized hardware to solve AI/ML problems will accelerate.

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.

In machine learning, ever-changing data, volatility, bias, and other factors require data science teams to manage models across their life cycle and monitor them in production. KPIs provide the anchor points in AI/ML projects by helping to define what outcomes we should expect when using the models to, say, improve a supply chain process. In that regard, the aggregated layers of KPIs provide a structure for decision-making and become critical to the success of the project. In an insightful interview, Bharadwaj sheds light on the nuanced relationship between AI, ML, and DevOps.

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.

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.

Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end automation. Think of streams of data as strands that run through the end-to-end supply chain to form a multi-layered fabric. Explore the future of AI on August 5 in San Francisco—join Block, GSK, and SAP at Autonomous Workforces to discover how enterprises are scaling multi-agent systems with real-world results. Gone are the days when on-premises versus cloud was a hot topic of debate for enterprises.

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.

• In machine learning, ever-changing data, volatility, bias, and other factors require data science teams to manage models across their life cycle and monitor them in production.
• Like monitoring applications for performance, reliability, and error conditions, machine learning model monitoring provides data scientists visibility on model performance.
• 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.
• As critical measures of operational performance, KPIs are fundamental to the efficiency of supply chains.
• The risk of being obsolete will render generic compute infrastructure for ML/AI unviable.

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.

The stakes are even more significant as AI and ML technologies increasingly take center stage when it comes to software development and management. Traditional software operations are giving way to AI-driven systems capable of decision-making, prediction, and automation at unprecedented scale. However, like any technology that ushers in new but immense potential, AI and ML also introduce new complexities and risks, elevating the importance and need for strong MLOps security. As reliance on AI/ML grows, the robustness of MLOps security becomes foundational to fending off evolving cyber threats.