Top 10 MLOps Tools to Optimize & Manage Machine Learning Lifecycle

Businesses continue transforming their operations to increase productivity and deliver memorable consumer experiences. This digital transition accelerates timeframes for interactions, transactions, and decisions. Additionally, it generates reams of data with brand-new insights into operations, clients, and competition. Machine learning helps companies in harnessing this data to gain a competitive advantage. ML (Machine Learning) models can detect patterns in massive amounts of data, allowing them to make faster, more accurate decisions on a larger scale than humans could. This enables humans and applications to take quick and intelligent action.

As more businesses experiment with data, they realize that developing a machine learning (ML) model is only one of many steps in the ML lifecycle.

What is Machine Learning Lifecycle?

The machine learning lifecycle is developing, deploying, and maintaining a machine learning model for a particular application. The typical lifecycle includes:

Establish a business objective

The first step in the process starts with determining the business objective of implementing a machine learning model. For instance, a business objective for a lending firm can be predicting credit risk in a certain number of loan applications.

Data Gathering & Annotation

The next stage in the machine learning life cycle is data collection and preparation, guided by the defined business goal. This is usually the longest stage in the development process.

Developers will select data sets for the model’s training and testing based on the type of machine learning model. Take credit risk as an example. If the lender wants to gather information from scanned documents, they can use an image recognition model; for data analysis, it would be snippets of numerical or text data gathered from loan applicants.

The most crucial stage after data collection is annotation “wrangling.” Modern AI (Artificial Intelligence) models require highly specific data analysis and instructions. Annotation helps developers increase consistency and accuracy while minimizing biases to avoid malfunction after deployment.

Model Development & Training

The building process is the most code-intensive element of the machine learning life cycle. This stage will be mostly managed by the development team’s programmers, who will design and assemble the algorithm effectively.

However, developers must constantly check things during the training process. It is critical to detect any underlying biases in the training data as quickly as possible. Assume the image model cannot recognize documents, forcing it to misclassify them. In this situation, the parameters should instruct the model to focus on patterns in the image rather than pixels.

Test & Validate Model

The model should be completely functional and running as planned by the testing phase. A separate validation dataset is used for evaluation during training. The goal is to see how the model reacts to data it has never seen before.

Model Deployment

It is finally time to deploy the machine learning model after training. At this point, the development team has done everything possible to ensure that the model functions optimally. The model can operate with uncurated low-latency data from real users and is trusted to assess it accurately.

Returning to the credit risk scenario, the model should reliably anticipate loan defaulters. The developers should be satisfied that the model will meet the lending firms’ expectations and perform properly.

Model Monitoring

The model’s performance is tracked after deployment to ensure it keeps up over time. For instance, if a machine learning model for loan default prediction was not regularly refined, it could not detect a new default type. It is critical to monitor the models to detect and correct bugs. Any key findings from the monitoring can be used to improve the model’s performance.

The Rise of MLOps

As we saw above, managing a complete lifecycle on a scale is challenging. The challenges are the same as those faced by application development teams when creating and managing apps. DevOps is the industry standard for managing operations during the development cycle of an application. In addressing these challenges with machine learning, businesses must take a DevOps-style approach to the ML lifecycle. This technique is known as MLOps.

What is MLOps?

MLOps is short for Machine learning + Operations. It is a new discipline requiring a mix of best practices in data science, machine learning, DevOps, and software development. It helps reduce friction between data scientists and IT operations teams to improve model development, deployment, and management. According to Congnilytica, the market for MLOps solutions to grow by nearly $4 billion by 2025.

Data scientists spend most of their time preparing and cleaning data for training purposes. Also, the trained models need to be tested for accuracy and stability.

This is where MLOps tools come into the picture. The right tool can help you manage everything from data preparation to deployment in a market-ready product. To save you time, I curated a list of the best enterprise and open-source cloud platforms and frameworks for managing the machine learning lifecycle.

Continue reading: Top 10 MLOps Platforms for Machine Learning Lifecycle Management.