Goodbye, manual work. Hello, Machine Learning Model Operationalization Management (ML Ops).
For the past decade, countless hours have been lost to the monotonous and manual tasks of turning data into insights. The industry is full of companies that spend too much time on this kind of work. As a result, they can’t create as many insights as they’d like, or generate insights that are as useful as they could be.
The good news: there’s a better way. And now it’s easier than ever to implement.
In this blog, we’re going to talk about ML Ops and how it can help you shift your focus from manual work to more valuable activities like creating better models and improving data quality along with this we are also going to talk about 4 tools that can be used to make the deployment and management of Machine Learning models in Python a breeze.
Nowadays, organizations and individuals acquire machine learning (ML) models from a variety of sources – and simply implement them, without thinking about model operationalization. It is similar to buying the latest smartphone and not bothering about getting a data package for it. Such an approach will leave your organization in a real trouble,in terms of costs and usage.
Manually model development can be an error prone process, especially if a simple change in the dataset requires a model port to another platform. If the data set is large enough, it gets even more complex. ML Ops solves these problems by providing integrated utilities at the model level to help users make transformations efficiently, right when developing a model.
It is becoming increasingly evident that machine learning (ML) is not only part of the future — it is here to stay, and this transformation has been fueled by the rise of data.
Deep learning algorithms are now beating humans at certain tasks in open competitions, which has changed views about machines hyping up the hype machine. You’ve been running experiments at scale for a while. Perhaps you have a well-oiled machine in your hands, maybe not. But there’s one thing for sure – as experiments increase in number and manual work decreases, you realize that the skills of your organization are quickly becoming very different than what they use to be. The growing complexity of the test matrix and result analysis is making the ‘one-size-fits-all’ manual testing approach less effective and viable.
ML Ops isn’t just a new way of developing machine learning models – it’s a whole new way of doing data science!
Automating machine learning (ML) work can reduce time to launch of a new product.
Do you have a machine learning model? Then it’s time to start thinking about operationalizing! Then, think of the possibilities:
- Your entire business can be run off of an algorithm
- You can have a robot that does your job for you
- You’ll save on operational costs by automating away all the people who do things manually
What do you have to lose? Look into ML Ops today!
Now lets talk about the curated awesome MLOps tools list that can kickstart your Journey
- Kubeflow:
Kubeflow is an open source MLOps solution that simplifies the orchestration and deployment of Machine Learning workflows. Kubeflow offers dedicated services and integration for Machine Learning phases like as training, pipeline building, and Jupyter notebook administration.
2. MLFlow:
MLFlow is a free and open source machine learning lifecycle management platform with features such as experiment tracking, project bundling, model deployment, and registry. MLFlow interfaces with a variety of Machine Learning libraries, such as TensorFlow and Pytorch, to make training, deployment, and maintenance of Machine Learning applications easier.
- Pachyderm:
Pachyderm is a Golang-based open source Machine Learning tool with over 5,000 ratings on GitHub. Pachyderm is a data science and machine learning version control tool. It’s also built on Docker and Kubernetes, which makes it easy to run and deploy Machine Learning applications on any cloud platform. - Metaflow:
To rapidly train, deploy, and maintain ML models, Metaflow unifies Python-based Machine Learning, Deep Learning, and Big Data libraries. Netflix created Metaflow, which is an open source MLOps platform. It’s a Python/R-based application that makes building and managing enterprise Data Science projects simple.
The machine learning model is an instrument that helps the businesses in collecting a big amount of data. ML Ops are the process which is followed while preserving the model.This tool can reduce the cost and time to deliver the model. The step by step process of machine learning operationalization includes, Load data into the operationalize framework, Prepare environment for model training or grid search optimizer, Training your model and Use the model.
ML Ops is the best solution for building and managing a production able machine learning model workflow. It is easy to configure and scales with your needs. Try it out yourself, you’ll be surprised at how fast you can create production ready machine learning models in your own systems! Hope you this article at MLdots
