In space movies, starship captains ask their board computers questions and receive detailed, meaningful predictions. In reality, artificial intelligence (AI) is no longer a sci-fi concept since at least 50 percent of companies are already using it for:

  • Product enhancement 
  • Manufacturing optimization  
  • Predictive maintenance  
  • Risk modeling  
  • Supply chain management  
  • And other business functions   

AI can help you make efficient managerial decisions, improve performance, and automate routine operations. How? — With the help of models. A significant part of AI is machine learning (ML)—a discipline about enabling computers to learn from data. The latter’s algorithms analyze data sets, detect patterns, and make conclusions with or without human intervention. A model is the output of this process.   

Enterprise ModelOps and MLOps

With models, you can peek into the future – for instance, predict your customer lifetime value, user engagement, or product popularity. But here is the catch: successfully building a model is only half the battle. To make it useful, you must deploy it. You need to integrate a model into your production environment, monitor and manage it 24/7, and ensure compliance with the existing regulations. Besides, models can quickly lose their predictive value due to changing circumstances such as the COVID-19 pandemic.   

To address these challenges, companies opt for MLOps and ModelOps. Let us see what makes them so highly demanded and why you should start using them, too.     

What is MLOps?  

The need for MLOps comes from a simple yet fundamental problem. Data scientists can elaborate machine learning models but cannot create software to utilize them. On the other hand, developers can build software products but lack the knowledge to embed models into applications. As a result, models are often isolated from the real world, and their practical value decreases. It’s worth noting that around 87 percent of ML projects never pass through the experimental phase.   

Meanwhile, MLOps can fix this for you.   

This concept comprises a set of practices for continuous adaptation of ML models to be used in the real world. Relying on the cross-functional collaboration between data scientists and operations specialists, along with CI/CD principles, MLOps can reduce the time needed to deploy new models to production from months to hours.   

This fact alone shortens your time-to-market and time-to-value dramatically. However, there are other advantages you can gain from MLOps implementation since it allows you to:  

  • Monitor your deployed models’ accuracy in real-time   
  • Manage thousands of models simultaneously, so retraining, redeploying, or replacing outdated models becomes more efficient  
  • Adapt your machine learning models to actual data and get through unexpected emergencies or anomalies safer  
  • Facilitate the process of training, testing, and deploying your models with the help of automation and versioning   
  • Set compliance frameworks for your developers to help them meet security, privacy, and regulatory requirements 
  • Eliminate bias and improve the overall model’s quality  

Companies that actively employ AI in their everyday business operations find all this convincing enough to start investing in MLOps solutions. Without them, models often remain nothing more than lab experiments, so it’s hardly surprising that the market is projected to reach $4 billion by 2025. With all facts considered, this number is likely to grow further.    

ModelOps: operationalize AI at scale  

MLOps is not a panacea when it comes to using AI solutions at an enterprise scale. Sooner or later, you will face the last mile problem of analytics, “How do I actually integrate AI into my business operations and decision-making process?”  

This is when the ModelOps approach comes into play. In brief, it is a strategy for governing all data science models released into production and managing their lifecycles up until retirement. Being a relatively new concept, ModelOps has generated enough buzz for companies to start implementing it. In 2021, 53 percent of enterprises already have dedicated budgets for ModelOps to pass the last mile effectively. Meanwhile, 37 percent plan to allocate funds within the next 12 months.

But what makes it so highly demanded?  

ModelOps minimizes coordination efforts and operational frictions between multiple stakeholders involved in model creation: CIOs, IT directors, data scientists, compliance specialists, DevOps and ITOps experts, and others. It also provides business stakeholders with tools (dashboards, reporting, and more) to autonomously assess the state and quality of models in production and make informed decisions without relying on data specialists.    

Some other advantages you can gain from ModelOps include:  

  • Enforcing new AI models’ governance and compliance 
  • Operationalizing and scaling AI solutions more effectively   
  • Monitoring the accuracy of already deployed models  
  • Updating or replacing outdated models in time   
  • Improving the auditability of your models 

By streamlining the practical application of your analytical models, ModelOps boosts their predictive efficiency and business value. McKinsey believes AI-powered analytics can potentially create the annual value worth up to $15.4 trillion.   

All this is possible only with ModelOps taking your analytics out of the lab and applying it to day-to-day business processes.   

FAQ  

What types of ML models are there?   

Machine learning models fall into two big categories:  

  • Supervised models, which use labeled training data to evaluate the accuracy of their outputs 
  • Unsupervised models, which learn through analyzing data and extracting patterns without human intervention  

Supervised models comprise:  

  • Classification models that predict binary outcomes (yes/no, true/false, male/female) 
  • Regression models that predict numerical values (age, height, prices, and more) 

Unsupervised models include: 

  • Neural networks 
  • Clustering 
  • Anomaly detection, and more 

There is also a reinforcement learning type: exposing a model to a specific environment to make it learn through trial and error. 

Are there any alternatives to MLOps and ModelOps? 

Currently, these are the most efficient methodologies when building an AI-powered business. We recommend you implement them, because: 

  • Without MLOps, you will not be able to operationalize machine learning models quickly 
  • Without ModelOps, you will have a hard time aligning AI with the enterprise scope 

Where should you start with implementing MLOps?  

There are three levels of MLOps maturity: 

  • MLOps 0. You build and deploy ML models manually 
  • MLOps 1. You automate the process of using new data to facilitate continuous retraining of your models  
  • MLOps 2. You automate CI/CD pipelines to retrain, update, and redeploy thousands of models at once, with minimal human intervention 

In brief, you need to gradually move your processes from one level to another until you reach MLOps 2. Or you can invest in ready-made MLOps solutions. The latter is faster and more affordable but the downside is that such solutions may not fully address the needs of your business.