What is MLOps? And why Data Science needs it

What is MLOps?

Angela de Loureiro
8 November 2022

I started off my data science career by enrolling in a few online short courses. Most of the courses concluded with exporting a perfectly tuned and optimized model in a pickle format. Now there is nothing wrong with this process of building a model, but I did soon realize that this only led to more questions that needed to be answered, like:

  • How do I scale my model?
  • How can I ensure that in the next six months to a year, my model is still providing accurate predictions?
  • How do I easily re-train my model with new data?
  • How can my stakeholders easily access these predictions?
  • How can I do this without this being a resource-intense exercise?

This then lead to the introduction of MLOps, where DevOps and Machine Learning joined forces.

What is DevOps?

Before getting into MLOps, let’s discuss DevOps. DevOps is the combination of development and operations. It is a set of practices that shorten the systems development life cycle and provide continuous delivery with high software quality. In a broader definition, DevOps promotes better communication and collaboration between these teams in an organization. DevOps is complementary to Agile software development; several DevOps aspects came from the Agile way of working.

What is MLOps?

MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. This is achieved through building an architecture that harmoniously works with the data science process. MLOps allows companies to easily deploy, monitor, and update models in production. Additionally, MLOps can also help you solve the following problems:

Deployment issues: Data scientists often spend a lot of time troubleshooting models during the deployment process. MLOps can help automate deployment.

Lifecycle management: Even if they can identify model decay, organizations cannot regularly update models in production. MLOps cuts through this process and streamlines it.

Complying with regulations: It can be difficult to stay up to date with the latest regulations. MLOps can help organizations stay up-to-date with shifting regulations.

Improved communication: MLOps helps bring machine learning workflows to production by improving communication between data science teams and operations teams.

MLOps Lifecycle

Now that we have models that can scale with MLOps, initial observations show the model is working. However, a model is only as good as its data. It’s the same principle that I have learned on all the cooking shows on Netflix – the dish is only as good as the ingredients. Before even considering an AI solution, it’s important to have data. We are living in a world where the expected total global data storage is likely to exceed 200 zettabytes by 2025.

This is where DataOps comes in.

What is DataOps?

DataOps is a set of practices, processes, and technologies that combine an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration. DataOps promotes a culture of continuous improvement in the area of data analytics, without compromising on data quality.

AI Solutions with Integrove 

At Integrove we understand data. We have experts who are able to build the architecture where data, machine learning, development, and operations work together in a continuous process that allows you to have access to relevant and up-to-date information.

Meet our data scientist

Angela de Loureiro is a Lead Data Scientist at Integrove. Passionate about data and the insights we can gain from data, she is in the process of completing her master’s degree in Data Science.

Her skills and forté:   

  • Ethical use of data
  • Automating the process
  • Builds and applies models and algorithms to mine data
  • Using past and present data to tell you about tomorrow
  • Turns data into knowledgeand the capacity to make due use of it

Over the last 8 years, Angela has relentlessly pursued a career working towards inspiring the next generation of data-savvy individuals, who share her passion where data is used to tell authentic stories that bring about real change.

Let us partner together you ensure that you get the most value from your data.  

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