Why Data Quality is Key to Successful ML Ops

Why Data Quality is Key to Successful ML Ops

In this post, we are going to look at ML Ops, a recent development in ML that bridges the gap between ML and traditional software engineering, and highlight how data quality is key to ML Ops workflows in order to accelerate data teams and maintain trust in your data.

In this first post in our 2-part ML Ops series, we are going to look at ML Ops and highlight how and why data quality is key to ML Ops workflows.

Machine learning has been, and will continue to be, one of the biggest topics in data for the foreseeable future. And while we in the data community are all still riding the high of discovering and tuning predictive algorithms that can tell us whether a picture shows a dog or a blueberry muffin, we’re also beginning to realize that *ML isn’t just a magic wand *you can wave at a pile of data to quickly get insightful, reliable results.

Instead, we are starting to treat ML like other software engineering disciplines that require processes and tooling to ensure seamless workflows and reliable outputs. Data quality, in particular, has been a consistent focus, as it often leads to issues that can go unnoticed for a long time, bring entire pipelines to a halt, and erode the trust of stakeholders in the reliability of their analytical insights:

”Poor data quality is Enemy #1 to the widespread, profitable use of machine learning, and for this reason, the growth of machine learning increases the importance of data cleansing and preparation. The quality demands of machine learning are steep, and bad data can backfire twice — first when training predictive models and second in the new data used by that model to inform future decisions.” (tdwi blog)

In this post, we are going to look at ML Ops, a recent development in ML that bridges the gap between ML and traditional software engineering, and highlight how data quality is key to ML Ops workflows in order to accelerate data teams and maintain trust in your data.

What is ML Ops?

Let’s take a step back and first look at what we actually mean by “ML Ops”. The term ML Ops evolved from the better-known concept of “DevOps”, which generally refers to the set of tools and practices that combines software development and IT operations.

The goal of DevOps is to accelerate software development and deployment throughout the entire development lifecycle while ensuring the quality of software by streamlining and automating a lot of the steps required.

Some examples of DevOps most of us are familiar with are version control of code using tools such as git, code reviews, continuous integration (CI), i.e. the process of frequently merging code into a shared mainline, automated testing, and continuous deployment (CD), i.e. frequent automated merges of code into production.

When applied to a machine learning context, the goals of ML Ops are very similar: to accelerate the development and production deployment of machine learning models while ensuring the quality of model outputs. However, unlike with software development, ML deals with both code and data:

  1. Machine learning starts with data that’s being ingested from various sources, cleaned, transformed, and stored using code.
  2. That data is then made available to data scientists who write code to engineer features, develop, train and test machine learning models, which, in turn, are eventually deployed to a production environment.
  3. In production, ML models exist as code that takes input data which, again, may be ingested from various sources, and create output data that’s used to feed into products and business processes.

mlops machine-learning data-quality data-science data-engineering data hackernoon-top-story artificial-intelligence

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Quality Data Drives the success of Machine Learning and Artificial Intelligence

History says the 16th century was the time during which the rise of Western civilization occurred. During this time, Spain and Portugal explored the Indian Ocean and opened worldwide oceanic trade routes, and Vasco da Gama was given permission by the Indian Sultans to settle in the wealthy Bengal Sultanate. Large parts of the New World became Spanish and Portuguese colonies

Most popular Data Science and Machine Learning courses — July 2020

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Artificial Intelligence vs Machine Learning vs Data Science

Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.

AI(Artificial Intelligence): The Business Benefits of Machine Learning

Enroll now at CETPA, the best Institute in India for Artificial Intelligence Online Training Course and Certification for students & working professionals & avail 50% instant discount.

Machine Learning Engineer vs Data Scientist (Is Data Science Over?)

Machine Learning Engineer vs Data Scientist (Is Data Science Over?) vs Data Analyst vs Research Scientist vs Applied Scientist vs…