MLOps Tools, Best Practices and Case Studies

A collection of procedures and methods known as MLOps are meant to guarantee the scalable and reliable deployment of machine learning systems. To reduce technological debt, MLOps uses software engineering best practices such as automated testing, version control, the application of agile concepts, and data management.

Using MLOps, the implementation of Machine Learning and Deep Learning models in expansive production environments can be automated while also improving quality and streamlining the management process. In this article, you will come across some of the tools and best practices that would help you do this job.

MLOps Best Practices

Following are the best practices of MLOps –

  • Taking a business concern and figuring out how machine learning can be used to solve it is the first stage in the machine learning lifecycle. There are quantifiable KPIs that show how successfully a business is reaching its goals. It is necessary to transform general business queries into performance indicators that the model may aim to achieve.

  • Modern working practices and organizational change are necessary for MLOps. This will occur only with time and the development of the organization's structures and procedures. This includes process modifications such as using DevOps for deployment or bringing on additional team members.

  • Selecting an ML model might be challenging. Before settling on a particular model, it is important to test several different ones and keep note of the findings. It's crucial to keep track of these models, perhaps by creating separate Git branches for each model. When trying to select the best model for production, this facilitates comparisons and selection.

  • After the model has been implemented, it is crucial to keep track of its performance to determine whether it is operating as anticipated. Once we have launched a Machine Learning model, a lot of things could go wrong. Finding any data change between the input and the goal variable by monitoring the model is helpful.

  • Models require system resources including CPU, GPU, I/O, and memory both during training and in use after deployment. Your team may optimize the cost of your trials and maximize your budget by being aware of the requirements of your system during the various phases.

MLOps Tools

Doing any of the best practices is good, but you would require a certain platform to perform such tasks. Below are some of the popular MLOps tools that could be helpful in your MLOps journey −

  • Neptune − It is a modeling tool that acts as a metadata repository for MLOps that aids in the better organization of ML metadata by research and production teams. It offers a centralized location for compiling, logging, storing, displaying, comparing, and querying all metadata produced throughout the machine learning lifecycle. The program allows tracking experiments, model registries, monitoring machine learning runs, and a robust dashboard for simple tracking.

  • KFServing − It is a model serving tool that is built on top of Kubernetes. KFServing standardizes ML operations and offers an API for inference requests. It offers a straightforward yet comprehensive narrative for production ML inference providing. It works with a variety of machine learning frameworks, including Tensorflow, XGBoost, ScikitLearn, and ONNX. It also abstracts away the complexity of server configuration, networking, health monitoring, and autoscaling.

  • Luigi − It is an orchestration tool developed by Spotify. It is a Python-based execution framework. It offers a toolbox with several work templates that are beneficial for teams. To reinforce the atomicity of operations and reliable data pipelines, the toolkit contains file systems abstractions for HDFS and local files. Additionally, it provides a variety of tools and utilities through a robust architecture, such as A/B test analysis, internal dashboards, and external reporting, to enable complex job pipelines.

  • MLFlow − It is a modeling tool that provides better flexibility and scalability to support both lone people and big businesses. Any language, ML library, and existing code are compatible with MLflow. It enables you to share your machine-learning code with others and provides a structure for reproducible runs using Docker and Conda. To effectively manage the whole lifecycle of MLflow models, a centralized model repository, user interface, and set of APIs are used.

  • DVC − It is a data tool with features like agility, data versioning, reproducibility, and sharing effectiveness, DVC tool makes data version management for ML projects simpler. Big data may be efficiently organized and accessed thanks to this experimentation tool. It simplifies the management of experiments using Git tags, branches, and metrics to select the best version and monitor experiment progress.

MLOps Case Studies

Following are the important study cases of MLOps –

  • To efficiently assign assignments to numerous customers and determine the best routes for them, Instacart employs machine learning to address the path optimization problem. This article explains the entire process and structure.

  • Optical character recognition and word detector are two independent parts of a straightforward application like a document scanner. Additionally, the end-to-end system needs extra phases for training and tweaking. Each requires a separate production pipeline. The team's efforts to gather data, including the creation of their own data annotation platform, are also covered in detail in this article.

  • This article provides an outstanding overview of Uber's end-to-end workflow, the areas in which machine learning is being used at Uber, and how their teams are set up. Uber employs machine learning extensively in their production.

  • Over 120 million people subscribe to Netflix as of 2018, with half of those people residing outside of the US. In this article, they discuss some of their technical difficulties and how they utilize machine learning to solve them, including how to forecast network quality, find anomalies in devices, and assign resources for anticipatory caching.