A Beginner's Guide to Machine Learning Operations (MLOps)

Introduction

 

Machine learning is advancing from vigorous research to qualitative business solutions, however, according to the report of Deeplearning.ai only 2% of the companies have  strongly deployd ML models in production in order to improve business performance and processes. It becomes very sophisticated to employ machine learning models successfully at large scale to attain maximization of business benefits. 

 

To get the solid equation of such complexity, an intersection of robust techniques should be required, the intersection and evolution of software development and powerful machine learning techniques leading to industrialized ML specific applications. The intersection is “Machine Learning Operations”, or MLOps as it is known.

 

The necessity for hefty machine learning applications has made the field of machine learning operations evolve with fast pace to align business models/operations with unique business requirements and regulatory managements. Being an emerging field of technology, MLOps is swiftly gaining momentum among data scientists and machine learning enthusiasts that distinguishes ML models governance from conventional software engineering.

 

Machine learning operations, or MLOps, is the board term signifying the process of creating, employing and upholding machine learning models, it is an interdisciplinary field concerned with machine learning, DevOps, and data engineering with an emphasis on making swift and effective means for machine learning techniques. Let's discuss deeply through this guide.

 

 

About MLOps

 

Specifically;

 

“MLOps should be a language, framework, platform, or infrastructure based omnipresent practices”.

 

“MLOps, or DevOps for machine learning, lets data scientists and IT professionals collaborate  and augment the process of model development and deployment in particular via testing, validation and monitoring of machine learning models with continuous enhancements”.

 

“MLOps follows a similar pattern to DevOps. The practices that drive a seamless integration between your development cycle and your overall operations process can also transform how your organization handles big data. Just like DevOps shortens production life cycles by creating better products with each iteration, MLOps drives insights you can trust and put into play more quickly.”(from)

 

MLOps depicts machine learning fabrication practices aimed to consolidate ML systems developments (Dev) and ML systems operations (Ops), practicing MLOps allows automation and monitoring of all stages/steps of ML system development including integration, testing, launching, employment and infrastructure management.

 

(Related reading: Machine learning tutorial)

 

MLOps has the following capabilities;

 

1. Focusing on consolidating the complete product launch cycle for machine learning as well as software applications.

2. Allowing data engineers for automated testing of machine learning artifacts such as data validation, machine learning models testing, ML models integration testing.

3. Letting the application of agile principles and methodologies to machine learning projects.

4. Supporting ML models and datasets to develop superiority in these models under continuous integration (CI)/ continuous delivery (CD) systems, and

5. Decreasing the technical debt across machine learning models.

 

The table below lists some of the MLOps’s practices that shows how the platform is associated with data engineering and DevOps. 

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How are DevOps, Data Engineering and MLOps related, Source

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Following the principles of DevOps, MLOps defines ML lifecycle in apaced with software development lifecycle (SDLC) to make more efficient workflow and to obtain effective outcomes focused to support the continuous integration, development, and delivery of AI models or ML models at scalable production for reproducible results.

 

Components of MLOps

 

The image below presents MLOps framework that constitutes different components of MLOps;

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MLOps Framework, Source

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1. Use case discovery: With the collaboration and involvement of business artifacts and data scientists, this phase includes defining business problems, making it a problem statement and objectives that can be addressed/solved with ML approaches with relevant KPIs (Key Performance Indicator).

2. Data Engineering: Connecting data engineers and data scientists, the phase incorporates accumulating adequate data from various sources and preparing data (processing/validation) for the modeling process.

3. Machine Learning pipeline: With designing and implementing a pipeline integrated with CI/CD, this phase allows data scientists to use pipelines for experimentation and model testing as well as maintain record of data & model lineage along with associated KPIs across the entire experiments.

4. Production deployment: This phase assures safe and flawless implementation into appropriate production server- public cloud, on-premise or hybrid server.

5. Production Monitoring: A proper monitoring is conducted for both model and infrastructure in this stage. 

• Models get monitored via tailored KPIs such as input data distribution or modifications in model performance. Triggers are made ready for active experimentation using new algorithms and hyper-parameters in order to develop a novel ML pipeline.

• Infrastructures are monitored as per memory and computation requirements and to scale up as per requirement.  

 

(Must check: MLOps vs DevOps)

 

Benefits of MLOps

 

MLOps can benefit in multiple ways;

 

1. Scaling:

 

MLOps practices are essential to scale up machine learning-driven applications of an organization.  

 

 

2. Trusting:

 

MLOps develop a bond of trust in order to control machine learning practices in dynamic environments and make a reproducible process via automation, testing and validation. It also boosts the accuracy, validity and productivity of ML models development. 

 

 

3. Compliance:

 

The regulatory and compliance section of operations is among the important functions in organization ecospace, especially with the commencement of ML in business perspectives, for instance, algorithmic accountability bill in New York city and GDPR in EU show up the increasing demand of machine learning regulations in common.

 

MLOps keep the tech team ahead of the curve with best practices and evolving regulation alliances. MLOps can regenerate models according to demand and compliance while ensuring the existing systems remain in regular compliance even if resulting models and ML pipelines might evolve. 

 

For example, a data experts team could concentrate over producing and employing models, acknowledging that the operations team has command over regulatory processes. 

 

(Suggested read: Compliance Testing)

 

 

4. Correct data usage: 

 

Considering how to manage effective business operations, MLOps heavily relies on big data. The method tends to improve products with each consecutive iteration leading to shortening development lifecycles and bringing out strong insights. 

 

MLOps also converges on plenty of feedback that assists in analyzing what is noise in data and which anomalies are subjected to attention.

 

 

5. Improved communication, seamless workflow:

 

Defined in well-structured format, MLOps follows a set of practices to unify the development cycle and operation process seamlessly to make it more efficient.  

 

In particular, the data science team has an extensive knowledge of data and the operations team well aware of business acuity. In this condition, MLOps fuels ML models efficiency coupled with expertise of both teams. This collaboration reduces obstacles produced by sophisticated algorithms as MLOps transform ML production pipelines to be more adaptable and flexible to coordinate KPI-driven models.

 

 

6. Reduce risks:

 

Due to inaccurate and deceptive models, businesses are facing continuous risk of undermining or losing customers’ trust, unable to reach target audience, etc. For example, training data and the available real-world complex data are different resulting in poor prediction making, as they are driven by models making them less liable, here MLOps reduces business risks. 

 

Also, MLOps prevent biases during model development, for example, missed opportunities, legal risks, and marginalized audiences. 

 

Formulating ML models’ performance is a bit challenging, and hereby MLOps are aimed to implement and attain ML model life cycle at its entirety across the organization including reduction of technical debt, early detection of little risks, quick resolution for model performance all as quickly to market as possible.    

 

(Recommended read: Top trends in software development)

 

Conclusion

 

MLOps is a systematic process of developing, deploying, and monitoring machine learning (ML) solutions. It is an engineering discipline applied to various industries and use cases.

 

MLOps acts as a ladder between collecting data and extracting actionable insights to add business value, a potent MLOps strategy facilitates best of data science practices and operations to streamline scalable and reproducible machine learning models and authorizing organizations to enter in new data-driven era and to derive machine learning and AI benefits in real-life. The solid integration of data science and DevOps leverages managers and individuals to be more agile and productive.