Learning the Boundaries of MLOps


Original Source Here

Learning the Boundaries of MLOps

What it takes to be an MLOps Engineer

Building a machine learning model is no walk in the park. It requires a data scientist to look at multiple aspects of an ML pipeline — data collection, label checking, representative sample generation, model selection, evaluation, and deployment.

Source: https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf

The machine learning model once deployed marks the start of another journey of rigor. Also, the skills required to build an ML model are not always similar to the ones required to maintain it.

Machine learning, by nature of the work, is extensively experiment-driven. Many experiments are conducted before deploying the best-performing model to production.

By now, you must have started sensing the messy part of MLOps — so many experiments, maintaining multiple versions of different models trained on different data.

The Power of MLOps

It is entirely possible that your best of the lot model doesn’t work as intended in the production environment. Hence the need for MLOps, which lies at the intersection of model development, engineering, and DevOps teams.

Source: By Cmbreuel — Own work, CC BY-SA 4.0

MLOps empowers you with faster deployment and auditable system releases. It focuses more on creating pipelines and maintaining machine learning model performance on a large scale. In short, MLOps requires the engineer to own the responsibility of enabling the model by taking the handover from the model development team of data scientists.

MLOps team consists of data scientists, analysts, and engineers. The team is responsible for defining and establishing the processes, architecture, checklists, system, and governance practices to operationalize the machine learning models.

MLOps has always existed but is now evolving with a formal structure leaving behind the days of putting Jupyter notebooks in production environments.

MLOps attends to the pain points of the organizations charting out the ML journey such as who will look after the model in production, jitters of moving to the cloud, how to decide which cloud infrastructure is appropriate, trusting the ML model to handle data at scale and still yield good predictive performance and many more.

Learning the boundaries, if they exist

There are no clear and defined boundaries of the work MLOps teams carry out, but the line of work shared below largely comes under its realm:

Source: Character vector created by macrovector — www.freepik.com
  • Quality code: The MLOps team is proficient in software development, hence they are expected to share best practices with the team such as writing utility and helper functions and building modular code that can help in conducting multiple parallel experiments.
  • What-if? The best model does not take a long time to become not-so-best in production. The prime reason could be the change in data distributions, incorrect evaluation of best model selection from the candidate models, or the best model was not rightly evaluated on the representative sample of production data.
  • Go-to team for ML processes: The MLOps team is responsible for the centralization of tools, processes, and workflows to unleash the power of AI.
  • Data and Model Governance: Machine learning is a data-intensive field, hence the MLOps team must have a strong command of database systems, data modeling, and data structures. Besides, no model comes with the guarantee of running as was expected from offline evaluations. MLOps need to establish multiple checkpoints across the ML pipeline, analyze and narrow down where model performance started deviating from its expected behavior.
  • Scaling up AI applications: Maintaining large, secure, and reliable applications that can handle data at scale.

What skills does an MLOps engineer possess?

The key issue is that the developer who has built the ML model is the closest to the code and its nuances. Once it is handed over to the MLOps team, extreme care is required to make them aware of the model assumptions and data characteristics and where the model can fail.

Currently, most data scientists are more focused on the model building but are not equipped to handle deployment workflow. Similarly, the ML engineers are skilled with the pure mechanics of model deployment but not so much with the internals of the algorithm.

So, let’s find out what skills an MLOps engineer requires to carry out such work.

Source: Businessman cartoon vector created by jcomp — www.freepik.com

If you want to hone MLOps skills and be market-ready, check out what the job demands:

  • Knows how to evaluate the right cloud stack and scale up the proof of concept to an enterprise-level application.
  • Should be comfortable with machine learning frameworks and explain the model outcome.
  • Must be able to collaborate with the data science and data engineering team to maintain and deploy end-to-end machine learning pipelines.
  • Can analyze appropriate tools, technology, and techniques required for Big Data projects.
  • Should be able to containerize and build the deployment pipelines for new modules, applications, and features.
  • Provisions and monitors a reliable, resilient, secure, and scalable computing environment that can host large-scale ML systems.
  • Not only design and build, but also optimize the applications, and orchestrate them with Docker and Kubernetes.
  • Implements cloud policies by identifying the compute requirements to reduce the operational overhead while maintaining the efficient utilization of resources.
  • Maintains model and data versioning control to track different model versions, along with the training data and meta-information like training hyperparameters.
  • Design proper evaluation metrics to validate the model.

Wow, that is certainly a lot. I hope this post helps you understand what are the roles and responsibilities of an MLOps engineer, and what skills are required to deliver them.

But the messy part is still not resolved yet — the whole journey of MLOps becomes much easier with the help of the right tools and platform. Comet is one such platform that manages the entire machine learning lifecycle by tracking, comparing, explaining, and reproducing your machine learning experiments.

That takes care of the major worries of model deployment. Besides, it has a multitude of benefits like faster integration, team collaboration, model registry, model debugging, and report generation.

Comet is your go-to solution to get full-stack visibility right from model design and development to experiments and models in production.

Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments. We pay our contributors, and we don’t sell ads.

If you’d like to contribute, head on over to our call for contributors. You can also sign up to receive our weekly newsletters (Deep Learning Weekly and the Comet Newsletter), join us on Slack, and follow Comet on Twitter and LinkedIn for resources, events, and much more that will help you build better ML models, faster.


Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot

%d bloggers like this: