The AI Chronicles – Overcoming Challenges & Embracing Best Practices in MLOps

In a recent episode of the AI Chronicles podcast titled – Demystifying Machine Learning Operations – Part 2, our host Siegfried De Smedt and MLOps expert Raja Rajenderan delved into the intricacies of Machine Learning Operations (MLOps). This blog summarizes the key takeaways from their discussion, providing valuable insights for both experienced professionals and newcomers to the field. Topics covered are: 
  • Challenges of productionizing machine learning models 
  • The right way to approach MLOps 
  • Emerging trends and technologies in MLOps 
  • Pitfalls of inadequate MLOps implementation 
  • Monitoring for efficient MLOps 
  • Achieving ROI with MLOps

Challenges in Productionizing Machine Learning Models

Productionizing machine learning models is often considered a challenging task. It is a well-known fact that only a small percentage, typically around five to ten percent, of models successfully transition from development to production. However, it is important to recognize that not all models need to make it to production for MLOps to be effective. The primary objective of MLOps is to enable quick prototyping and validation, following the fail-fast principle discussed in previous podcasts. MLOps serves a dual purpose: to ensure thorough testing and deployment of reliable machine learning models, as well as to prevent the integration of flawed models into production systems. Striking the right balance between these two goals is what makes operationalizing machine learning models challenging. The aim is to bring only good models into production while avoiding the deployment of inadequate ones. To overcome these challenges, it is essential to implement MLOps in the right way, utilizing best practices. By following established guidelines and leveraging industry expertise, organizations can optimize their MLOps processes. This involves employing rigorous testing methodologies, incorporating feedback loops, and adhering to industry standards.

The Right Way to Approach MLOps

When it comes to adopting the right approach for MLOps, there is no one-size-fits-all solution that works for every organization and scenario. However, organizations can benefit from complying with established frameworks to guide their MLOps practices. Two popular frameworks in the field of machine learning projects are CRISP-DM (Cross Industry Standard Process for Data Mining) and TDSP (Team Data Science Process) developed by Microsoft. By choosing one of these frameworks, organizations can gradually mature in their MLOps journey and ensure a structured approach. CRISP-DM is designed specifically for data mining projects aimed at extracting insights from data. It follows a six-step planning methodology, starting with business understanding, then progressing through data understanding, data preparation, modeling, evaluation, and deployment. TDSP, on the other hand, focuses on the end-to-end lifecycle management of data science projects, including machine learning. It shares similarities with CRISP-DM but follows a five-stage process. It begins with business understanding and then moves on to data acquisition, data understanding, modeling, and deployment. Notably, TDSP includes a final step called customer acceptance, which differentiates it from CRISP-DM. While Microsoft’s TDSP integrates well into the Microsoft ecosystem, it doesn’t necessarily mean it is the best choice for every organization. CRISP-DM, being a more generic framework, can also be used effectively, even within the Microsoft ecosystem. Ultimately, the selection between CRISP-DM and TDSP depends on the organization’s specific needs and preferences.

Emerging Trends and Technologies in MLOps

Two emerging trends in the field of MLOps are model explainability and interpretability, as well as MLOps in edge computing. Model explainability and interpretability have gained significant importance in MLOps projects. Ensuring trust in AI systems requires incorporating these aspects into the development process. Open-source techniques and libraries such as SHAP and LIME facilitate the implementation of explainability in MLOps pipelines. This field is still evolving, as machine learning models, particularly neural networks, were once considered black boxes. However, the launch of AI-based systems that impact business decision-making necessitates transparency and accountability. Models must be able to provide insights into their predictions or decisions, enabling common users to understand and trust the AI system. Implementing model explainability and interpretability has become a priority in many projects, successfully enhancing transparency. Another area of growing interest is MLOps in edge computing. Edge computing involves using machine learning models at the edge, often in Internet of Things (IoT) applications. In edge computing scenarios, models are trained and even retrained on the devices themselves, eliminating the need to send data to the cloud for training. However, managing the training pipeline, data collection methodology, and model deployment strategy at the edge require a different approach to ensure accuracy and keep models up to date. One of the main challenges in edge computing is data synchronization, particularly in distributed environments with multiple edge devices or servers. Defining an MLOps process that can handle data synchronization and maintain model consistency across edge devices is crucial in overcoming this challenge.

Monitoring for Efficient MLOps

Monitoring is a critical aspect of MLOps. To ensure efficient MLOps, teams must proactively identify and monitor potential issues or areas of concern. While monitoring is typically addressed towards the end of an implementation phase, it is crucial to design monitoring upfront to the extent possible. An effective ML system should monitor for various issues, including model degradation, inaccurate predictions, and biases. These metrics and problems serve as indicators of system performance and should be closely monitored. Neglecting to monitor them can result in an ineffective monitoring system that fails to identify and address issues. Technically, ML models are susceptible to different types of drifts, such as model drift, data drift, feature drift, and upstream drift. Monitoring these drifts is essential. Additionally, monitoring data quality, including identifying skews and outliers, is important for maintaining model accuracy.

The Pitfalls of Inadequate MLOps Implementation

Collaboration and learning from failures are two crucial aspects often overlooked in MLOps practices. Firstly, collaboration is essential in any operational team, whether it’s ML Operations, DevOps, or API Operations. Lack of collaboration leads to problems within projects. While tools are often considered the solution to operational challenges, the primary focus should be on fostering collaboration. The principle of MLOps emphasizes collaboration between various team members, including data scientists, data engineers, DevOps engineers, web developers, application developers, and stakeholders, which may include business stakeholders. Regardless of whether the project follows CRISP-DM or TDSP frameworks, collaboration is required at every stage. This ensures that the whole team is involved in business understanding, data understanding, and deployment. Failure to foster collaboration can result in siloed efforts, reducing the effectiveness of deploying machine learning models. Therefore, collaboration is a key requirement for successful MLOps implementation. The second area where MLOps often falls short is the failure to learn from failures. Just as machine learning models learn from experiments, ML project teams should learn from the MLOps process. Without a well-defined MLOps process, the opportunity to learn from failures is missed. Conducting proper post-mortem analysis is a good practice to understand the reasons behind failures. By utilizing post-mortem analysis reports, teams can define and refine their MLOps framework. When MLOps is not established correctly, even learning from failure can lead to incorrect insights. Therefore, it is crucial not to neglect the importance of learning from failures as a key component of successful MLOps implementation.

Achieving ROI with MLOps

Implementing MLOps brings several benefits, and one of the primary advantages is achieving faster time-to-market. This means organizations can capitalize on market opportunities more quickly and gain a competitive edge. To ensure this, the involvement of business leaders is crucial in the initial stages of the MLOps process. Business leaders play a pivotal role in defining clear objectives and outcomes. They need to clearly communicate their expectations to the Data Science Project team. By doing so, the team gains a comprehensive understanding of the desired outcomes, which helps avoid delays and misunderstandings. Once the business objectives and success criteria are established, the project team can proceed with the necessary steps. This includes conducting a visibility study, creating a robust implementation plan, aligning with stakeholders, and performing a thorough cost-benefit analysis. These steps set the foundation for a successful MLOps implementation. When it comes to measuring success in MLOps, it is important to distinguish between outputs and outcomes. Outputs refer to the technical metrics commonly used in machine learning, such as accuracy, precision, recall, F1 score, and area under the curve. These metrics can be monitored and visualized using dedicated dashboards, providing insights into the performance of the machine-learning models. However, outcomes are equally important. They represent the real-world impact and value generated by the ML initiative. For example, if the goal is process automation, the outcome can be measured by quantifying the actual cost or time saved through the automated process. This can be achieved by establishing baseline measurements of cost and time associated with the manual process, and then comparing them with the metrics obtained from the automated process. This comparison demonstrates the tangible return on investment and the benefits of implementing MLOps.


In conclusion, navigating the challenges of productionizing machine learning models and embracing MLOps requires a holistic approach, encompassing collaboration, learning from failures, proactive monitoring, and a focus on measurable outcomes. By adopting best practices and staying abreast of emerging trends, organizations can unlock the full potential of MLOps and harness the transformative power of machine learning in their operations. To listen to this podcast episode, click here – 
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