MLOps is defined as "a practice for data scientists and operations experts to collaborate and communicate to help manage the production ML (or deep learning) lifecycle." MLOps boosts the automation and improves the quality of production ML while simultaneously concentrating on business and regulatory needs, similar to DevOps or DataOps."
Still confused?
MLOps offers the insights that you can rely on and can put them into action more rapidly, just as DevOps simplifies production life cycles by delivering better products with each iteration.
In short, MLOps stands for Machine Learning Operations combined with DevOps to develop solid automation, tracking, monitoring, pipelining, and packaging solution for Machine Learning models.
The topline benefit of machine learning is an organization's ability to stay relevant and develop in today's digital and information-driven environment, among many other benefits. This capacity gets enhanced expeditiously when integrated with operations to form MLOps.
There are numerous positive impacts of MLOps, and a few of them are:
- Machine learning lifecycle management that allows for rapid innovation
- Make workflows and models that are repeatable.
- High-precision models may be deployed in any area with ease.
- The complete machine learning lifecycle is well managed.
- Control and administration of machine learning resources.
The below image depicts the process of MLOps as a whole:
Below is the list of the top 5 Tool for MLOps, that are assisting businesses and individuals in their growth.
- Kubeflow
Kubeflow aims to make machine learning (ML) workflow deployments on Kubernetes as simple, portable, and scalable as possible. Its goal is to make the deployment of best-of-breed open-source machine learning systems easy and simple on a variety of infrastructures. Kubeflow can be run anywhere Kubernetes is installed.
Benefits of Kubeflow:
- Create and manage interactive Jupyter notebooks with Kubeflow's services. You may tailor your notebook deployment and compute resources to your specific data science requirements.
- You may train your machine learning model with Kubeflow's custom TensorFlow training job operator. Kubeflow's job operator, in particular, can handle distributed TensorFlow training jobs.
- To export trained TensorFlow models to Kubernetes, Kubeflow provides a TensorFlow Serving container.
- Kubeflow Pipelines is a complete solution for delivering and managing machine learning processes from start to finish.
- Kubeflow goes beyond TensorFlow in terms of support. PyTorch, Apache MXNet, MPI, XGBoost, Chainer, and other libraries are supported.
2. MlFlow
MLFlow is an open-source platform that allows you to manage the entire machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. It is integrated with a variety of Machine Learning libraries, such as TensorFlow, Pytorch, and much more. This integration makes training, deployment, and maintenance of Machine Learning applications easier.
Benefits of MLflow:
- Data science code is packaged in a way that allows it to be reproduced on any platform.
- Machine learning models can be used in a variety of service scenarios.
- In a central repository, you may save, annotate, discover, and manage models.
3. Data Version Control (DVC)
DVC is an open-source Data Science and Machine Learning application built in Python. It uses a Git-like paradigm to handle datasets and machine learning models, as well as versioning them. It makes machine learning models reproducible and shareable. It's built to work with huge files, data sets, machine learning models, metrics, and code.
Benefits of DVC:
- Machine learning models, data sets, and intermediate files are all version-controlled. DVC employs code to connect them and stores file contents on Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, or disc.
- A project in DVC has a cleaner structure since it permits branching as simple and fast as Git — regardless of the size of the data files.
- Lightweight pipelines are introduced by DVC. It allows you to utilize push/pull commands instead of ad-hoc scripts to transport consistent bundles of machine learning models, data, and code into production, distant computers, or a colleague's machine.
- Every ML model's whole evolution may be tracked with full code and data provenance.
4. Metaflow
Netflix created a Python/R-based application named Metaflow, which was made open-source in 2019. It makes building and managing enterprise Data Science projects simple.
Metaflow simplifies the creation and management of real-world data science initiatives. To rapidly train, deploy, and maintain ML models, Metaflow unifies Python-based Machine Learning, Deep Learning, and Big Data libraries.
Benefits of Metaflow:
- Metaflow assists you in designing your workflow, scaling it, and deploying it to production.
- It automatically versions and tracks all of your experiments and data.
- It makes it simple to inspect findings in notebooks.
- Metaflow has built-in connections with Amazon Web Services' storage, computation, and machine learning services.
5. Pachyderm
Pachyderm is a version-control tool for Machine Learning and Data Science that works similarly to DVC. It's built on Kubernetes and Docker, which makes it easy to run and deploy Machine Learning applications on any cloud platform. Every piece of data input into a Machine Learning model is versioned and retraceable with Pachyderm.
Benefits of Pachyderm:
- It specializes in structured data, which allows for an AI-driven business model.
- Models can be easily built on top of the data warehouse.
- NLP should be accelerated. Data-driven automation for development
- Handle even the largest unstructured and structured data sets with ease.
- Reduce model risk by ensuring complete reproducibility.
The Bottom Line
Effectively using machine learning is more than crunching numbers or trusting your data scientists to figure out compliance and business intelligence on their own. It's critical to take ownership of production-level machine learning so that your operations staff understands and knows this new era of data which will help the data team focus on what they do best. Looking forward to operations ensures that you're ahead of the machine learning curve and that your adoption is seamless and insightful right away.
MLOps is one of the most helpful practices a company can have because it automates everything from data sourcing, data processing, analysis, scalability, auditing, and prediction monitoring. It helps the organization in production model deployment, model monitoring, life cycle management of the model, and the governance of the model as well.
Many open-source frameworks have arisen in the few short years that pushed MLOps to gain prominence. As technology and data continue to reach new heights, implementing solid ML strategies now will help enterprises of all types manage and prosper in the future.
