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Three Crucial MLOps Techniques for Digital Transformation

By Umme Sutarwala - November 02, 2022 4 Mins Read

With inquisitiveness in Machine Learning Operations (MLOps) growing dramatically over the past two years, it is now a cornerstone for many data leaders and practitioners. This sharp increase is a result of a problem that many organizations are grappling with: investments in Machine Learning (ML) and Artificial Intelligence (AI) are not yielding the promised Return on Investment (ROI).

Organizations are investing in AI and machine learning across all industries in order to get business insights and make data-driven choices. Working with corporations in a variety of industries, industry experts have found that just half of AI evidence of concepts ever rises to production. Using Machine Learning Operations (MLOps), many enterprises may significantly boost their success rate.

Enterprises should think about concentrating on the following practices if they want to profit from adopting MLOps best practices as part of a digital transformation for AI and business executives.

Also Read: Why Organizations Require an Edge Data Architecture to Digitize Sustainably

Create an ML team with a broad range of skills

While there is no one way to create a team for marketing, HR, or ML, promoting a culture of collaboration and communication is crucial. A digital transformation driven by AI requires more than just talent, so keep that in mind as well. Additionally, there is organizational change management.

Building a team of specialists from multiple fields is crucial for businesses advancing their AI transformation. A team might include professionals in their respective fields, data scientists, ML engineers, ML architects, etc.

It is the responsibility of AI leaders to harness the creativity and strength of people with a variety of skill sets, especially in light of the rapid technological advancements and the widespread adoption of creative ideas. For instance, take into account employing folks with expertise in DevOps, cloud ML engineering, and ML architecture design, to mention a few.

Deploy automation in processes

Automation and the idea of maturity models go hand in hand. Increased MLOps maturity for an organization is facilitated by advanced and pervasive automation. Many jobs within machine learning systems are carried out manually in environments lacking MLOps. These tasks may involve feature engineering, separating training and testing data, data cleansing and transformation, building model training code, etc. Data scientists squander time that would be better spent on experiments by performing these processes manually.

Continuous retraining, where data teams may set up pipelines for data ingestion, experimentation, validation, model testing, feature engineering, and more, is a fantastic example of automation. Continuous retraining, which is frequently regarded as one of the initial phases in machine learning automation, prevents model drift.

MLOps pipelines are identical to data engineering or DevOps pipelines. An organized flow of data into and out of an ML model is called an ML pipeline.

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Firms can envision an instance where data scientists manually extracted some of the data but were able to automate their validation, model training, data preparation, and evaluation procedures to demonstrate the value of pipelines. When the model is productionized, it can be used again to produce predictions based on new data.

Comprehend the maturity of MLOps

Leading cloud providers employ an MLOps adoption maturity model. MLOps installation calls for organizational change as well as new working procedures.

This gradually occurs as the organization’s processes and practices begin to take shape.

An honest assessment of the organization’s MLOps maturity development is necessary for any effective MLOps implementation. Companies can understand how to advance to a maturity level after undertaking a reliable maturity assessment. This includes adjusting the deployment procedure, including deploying DevOps or adding additional team members.

A feature store is just one of the many ways to store data for machine learning. Organizations with a somewhat well-developed data infrastructure benefit from feature stores. They must guarantee that similar features are utilized by various data teams in order to minimize effort duplication. If a company just has a small number of data scientists or analysts, creating a feature repository might not be worthwhile.

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AUTHOR

Umme Sutarwala

Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain

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