Major Challenges of Machine Learning DevOps

As enterprises embrace machine learning, many people are trying to squeeze into their Software Development Life Cycle.

The problem is that machine learning is significantly different from traditional apps, and we need to account for that as we make the changes. Read on for the significant challenges that you need to address in time to develop an infrastructure that can support your enterprise as you scale.

Making Heterogeneity Work

Machine learning (ML) can only be successful if an enterprise selects the right tool for the job at hand. Versioning and consistency can become problematic since there are several frameworks and toolkits in every programming language. In other words, different frameworks can work with a given programming language, but they are tuned for different operations.

To avoid this challenge, choose the right tools for each of your tasks. Data scientists can rely on the appropriate use case and choose Scala, R, Python, or an alternative language to build a particular model. They can also choose a different language for another model to give you the desired results.

Planning for ML Iterating

Every code in machine learning forms a small part of a large ecosystem. The interactions of the live data with the new can bring about a significant model drift and adversely affect accuracy, making ML iterations much faster than traditional application development workflows. The resultant change can place greater demands on several operational processes, including versioning. Besides, it may require a high degree of agility from DevOps.

Managing Composability

Many modern apps are built from multiple components. Machine learning is one of the most composable technological solutions out there today. Its building blocks are disparate and granular.

Unlike traditional applications, machine learning-enabled apps have interconnected models that generate the core logic that different teams write using a wide variety of frameworks and languages. For example, an agency that wants to identify news and reports essential for the success of one of its customers can use the following tools to achieve the goal.

Use Optical Character Recognition model to extract the required information from scanned documents
Use a language-identifier model to make use of the language of the text
Use software to translate the text into English
Use vectorization to convert the English texts to numbers

They can also score the information using a sentiment analysis model

Performance and Success Metrics

Traditional app performance metrics are known to be incredibly easy to define, evaluate, and track, but you may find it quite challenging to optimize those goals. Measures of success, such as increased frames per second and faster response times, are easy to understand. Moreover, you can find that benchmarking adjustments in these areas are straightforward though they vary from one project to another.

However, that is not the case with ML-based performance. It is multidimensional and integrates multiple types of metrics. As such, one requires a high degree of expertise to be able to track the performance accurately.

Conclusion

Technology catalyzes enterprise digital technologies in our present service-oriented world. At the software lifecycle’s development and deployment, nothing is as important as the efficient collaboration between development and operations staff. But there are several machine learning DevOps challenges that can limit the efficiency of apps and cloud services. If you understand them, you will build an infrastructure that can support your business as you scale.

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