The ultimate guide to MLOps tools and how MLOps drives business? 

MLOps tools

Last Updated on February 14, 2024 by Nasir Hanif

What is MLOps? 

Let’s dive in; 

The Ultimate Guide to MLOps Tools 

MLOps is a collection of practices needed for collaboration and communication in-between operations professionals and data scientists. Following set practices maximizes the quality while at the same time simplifying the management process. Not only that but following MLOps also helps automate the deployment of machine learning and deep learning models in large-scale production settings. With MLOps tools, it becomes easier to align the model with business needs and regulatory requirements. 

The ultimate guide to MLOps tools explains; 

Where are the MLOps and MLOps tools headed? 

MLOps is evolving into an independent approach to machine learning management. It relates to the whole lifecycle, including;

  • Model Creation – software development and continuous integration  and delivery 
  • Data Gathering 
  • Deployment 
  • Health 
  • Orchestration 
  • Diagnostics 
  • Business Metrics 
  • Governance 

What are the key phases of MLOps? 

  1. Data gathering
  2. Data transformation/preparation
  3. Data analysis
  4. Model validation 
  5. Model monitoring 
  6. Model training & development 
  7. Model serving 
  8. Model re-training.

How Does MLOps Tools Drive Business Success?

MLOps is the fusion of operations and machine learning. It is a set of methods used to automate the cycle of machine learning algorithms in production time, from the initial model training to deployment and then again to retraining for the new data.  The MLOps tools facilitate the collaboration between IT professionals and the data science team, hence combining techniques, skills, and tools to be used in machine learning, data engineering, and DevOps – the predecessor of the MLOps in software development. However, the most important part of a business integrating ML in their operations – is the M-tools. 

What are MLOps tools?

The MLOps tools help businesses apply the DevOps practices to the process of creation and use of the AI/ Machine Learning – ML model. Machine learning engineers, DevOps engineers and data scientists use MLOps tools. 

Benefits of MLOps Tools

There are plenty of benefits to the adoption of MLOps tools in businesses;

Short Time Marketing ML Models 

MLOps tools have introduced automation to model training and retraining processes. M-tools establish continuous integration and constant delivery – CI/CD, practices for deployment and updation of ML pipelines. The ML solutions facilitate making production faster. 

Improved User Experience 

Owing to the continuous training and model monitoring of MLOps practices – ML and AI-fueled apps get timely updates, consequently boosting customer satisfaction. 

Quality of Predictions

The MLOps tools take care of model validation, data, and performance evaluation in production while retaining fresh datasets. All of these combined eliminate the risks of false insights and guarantee that the algorithm-produced results are trustworthy – when one needs to make important decisions. 

How is MLOps different from DevOps?

MLOps and DevOps have some central similarities as the MLOps were derived from the principles of DevOps. However, both of these are different in execution. 

Experimental Nature of MLOps

In contrast to DevOps, the MLOps tools and platforms are much more experimental. ML/DL engineers and data scientists work on tweaking various features – models, hyperparameters and parameters. While keeping track of and managing data with the code base for reproducibility of experiments. Even with the combined efforts and tools, the ML industry still faces challenges with the reproducibility of experiments – but that being out of scope for this post, is another discussion for another time. 

Hybrid Composition of Teams

The team that builds and deploys the models in production are no longer composed of software engineers only. In an ML project, teams comprise of data scientists and ML researchers – who, as a unit, focus on exploratory data analysis, model development, and experimentation. Both data scientists and ML engineers might not be as experienced as software engineers – who build production-class services. 

Improved Testing

Testing ML systems/models include model training, model validation, and so on – in addition to traditional code tests, like integration and unit testing. 

Automatic Deployment 

One can’t simply deploy an offline trained ML model as a prediction service. For doing so, one will need a multi-step pipeline for automatically retraining and deploying a model. Note that this pipeline will add complexity as you must also automate the steps that data scientists perform manually before any deployment to validate and train new models. 

Final Thoughts: 

Why does MLOps matter?

MLOps tools are essential as they are fundamental. Machine learning aids businesses in deploying solutions that unlock the formally untapped sources of revenue, saving time and reducing costs by generating efficient workflows. All of that with leveraging data analytics for informed decision making and maximizing positive customer experience. 

Such goals are hard to accomplish if lacking a solid framework. Automation of model development and deployment with the M-tools offer faster go-to-market times with lower operational costs. All of this combined helps developers and managers to be more strategic and agile in decision making. 

How to start with MLOps tools? 

MLOps tools serve as a map for guiding small teams, individuals and even businesses to easily achieve their goals regardless of the sensitive data, limited resources, small budget and many other constraints. It is totally up to you to decide how big you want your map to become – as M-tools and practices are not written in stone. 

For more information on MLOps tools and practices, visit Qwak.

Apart from this, if you are interested to know about What Does a DevOps Engineer Do? then visit our Business category.