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 9 MLOps Research Papers You Should Read Now: Stay Ahead of the Curve and Start Implementing Today!
9 MLOps Research Papers You Should Read Now: Stay Ahead of the Curve and Start Implementing Today!

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MLOps is quickly becoming one of the most popular technologies in the field of machine learning. This is due to its ability to provide an efficient and effective way to manage, automate, and deploy machine learning models in production environments. With the help of MLOps, companies can move beyond traditional software development practices to create robust and reliable machine learning pipelines.

Are you looking to stay ahead of the game and learn about the latest Machine Learning (ML) Operations (MLOps) research? If so, look no further! In this blog post, we’ll cover Nine top MLOps research papers that will help you stay up-to-date on the most recent MLOps trends. We’ll provide a brief overview of each paper, along with its key takeaways and link for easy reference. So let's get started!

1️⃣ Machine Learning: The High-Interest Credit Card of Technical Debt https://bit.ly/3ktX2Xw

✅ Argues that quick wins from ML come with a cost in terms of ongoing maintenance costs at the system level

✅ Identifies ML-specific risk factors and patterns to avoid, such as boundary erosion, entanglement, and hidden feedback loops

 

2️⃣ Machine Learning Operations (MLOps): Overview, Definition, and Architecture: https://bit.ly/3kzz3q1

✅ Provides an aggregated overview of the necessary principles, components, and roles for MLOps

✅ Offers guidance for ML researchers and practitioners looking to automate and operate ML products

 

3️⃣ Operationalizing Machine Learning: An Interview Study: https://bit.ly/3R03amG

✅ Examines unaddressed challenges and implications for tool builders in ML deployment and maintenance

✅ Summarizes common practices for successful ML experimentation, deployment, and sustaining production performance

 

4️⃣ How to avoid machine learning pitfalls: a guide for academic researchers https://bit.ly/3D6ropr

✅ Outlines common errors in the use of ML techniques and ways to avoid them

✅ Focuses on issues of particular concern within academic research, such as the need to make rigorous comparisons and reach valid conclusions

 

5️⃣ Quality issues in Machine Learning Software Systems: https://bit.ly/3Wt0DlO

✅ Investigates the characteristics of real quality issues in ML software systems from the practitioner’s viewpoint

✅ Identifies a list of bad practices related to poor quality in ML software systems

 

6️⃣ Training Transformers Together: https://bit.ly/3GX88fd

✅ Proposes a method for training multiple transformer models simultaneously, resulting in improved performance and faster training times

✅ Demonstrates the effectiveness of this method on several benchmark datasets

 

7️⃣ Machine Learning Operations (MLOps) in the Wild: A Study of Practice: https://bit.ly/3wm5Cdq

✅ Examines the current state of MLOps in the industry, including standard practices, challenges, and tools used

✅ Identifies areas where research and development can improve the MLOps process

 

8️⃣ Machine Learning in Production: A Case Study: https://bit.ly/3HjABNK

✅ Provides a case study of the ML production process at a large technology company

✅ Discusses the challenges and best practices for deploying ML models in production environments

 

9️⃣ Machine Learning Productionization: A Survey: https://bit.ly/3iSVA0m

✅ Surveys industry practitioners to understand the current state of machine learning productionization

✅ Identifies common challenges and best practices in deploying ML models in production environments

 

 

MLOps provides an efficient and effective way to automate, manage and deploy ML models in production environments. With the help of these Nine research papers, you can stay ahead of the curve on the latest trends related to MLOps. From exploring unaddressed challenges for tool builders to outlining common errors in using ML techniques and identifying bad practices related to poor quality in software systems, this article has provided a comprehensive overview of what it takes to successfully implement machine learning operations into your workflow. Whether you’re looking for guidance or just want some extra reading material, these resources are surefire ways to ensure that your team is up-to-date with current developments in Machine Learning Operations (MLOps). 

 

 


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Prem Naraindas
Founder & CEO

Founder & CEO of Katonic.ai. Pioneering no-code Generative AI and MLOps solutions. Named one of Australia's Top 100 Innovators by "The Australian." Forbes Tech Council member, LinkedIn Top Voice 2024 , Advisor to National AI Centre. Previously led blockchain and digital initiatives at global tech firms. Katonic.ai: Backed by top investors, featured in Everest Group's MLOps PEAK Matrix® 2022. Passionate about making AI accessible to all businesses. Let's connect and shape the future of tech! #AIInnovation #TechLeadership #AustralianTech

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