With the influx of data science innovations and advancements in AI and compute power, the autonomous learning of systems has grown leaps and bounds to become an essential part of operations. The possibilities are endless and the result is that many organizations dedicate entire teams to ML operations.

MLOps in a nutshell

Machine learning operations (MLOps) is the use of machine learning models by development/operations (DevOps) teams. MLOps seeks to add discipline to the development and deployment of machine learning models by defining processes to make ML development more reliable and productive.

The development of machine learning models is inherently experimental, and failures are often a part of the process. The discipline is still evolving and it is understood that sometimes even a successful ML model may not function the same way the next day. Documenting reliable processes and creating safeguarding measures to help reduce development time can create better models.

The MLOps development philosophy is used by those who develop machine learning models, those who deploy them and those who manage the infrastructure that supports them. Standard practices for MLOps include:

  • Starting with existing product API from existing AI services.
  • Taking a modular approach.
  • Running parallel model development, halving the problems if a single model fails.
  • Having pre-trained models ready to show proof of concept.
  • Generalized algorithms showing some success can be further trained for their specific task.
  • Bridging gaps in training data with publicly available data sources.
  • Taking time to develop generalized AI in order to broaden opportunities.

What it is all about ...

"... help organizations become AI-driven – from the development of models through to the deployment and management of models in a variety of production environments.!"


3 Things you need to run MLOps

MLOps is defined as “a practice for collaboration and communication between data scientists and operations professionals to help manage production ML (or deep learning) lifecycle. Similar to the DevOps or DataOps approaches, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements.”

In short, MLOps is all the engineering pieces that come together and often help to deploy, run, and train AI models. 3 things are of primary importance:

  • Machine Learning
  • DevOps (IT)
  • Data Engineering

Each component contributes key elements that work to close the ML lifecycle loop within an organization.

With origins in the development of practices used to help data scientists and DevOps teams better communicate using machine learning, MLOps began as simple workflows and processes to deploy during implementations in order to manage the difficulties faced with ML. Much progress has been made since the beginnings. The benefits of dependable deployments and maintenance of ML systems in production are enormous. No longer just simple workflows and processes, now full-on benchmarks and systemization. IT and Data teams in all sorts of industries are trying to figure out how to better implement MLOps.

“MLOps follows a similar pattern to DevOps. The practices that drive a seamless integration between your development cycle and your overall operations process can also transform how your organization handles big data. Just like DevOps shortens production life cycles by creating better products with each iteration, MLOps drives insights you can trust and put into play more quickly.” (from:

When considering data as a key business tool that directly relates to how an organization adapts future system operations, essentially MLOps is the process of taking both data and code in order to produce predictions that describe which deployment to put into production. This requires both operations (code) and data engineering (data) teams to work hand in hand.

Securing the investment in AI

The aptitude for enterprises to scale AI-driven applications is dependent on the ability to quickly create world-class models combined with the ability to manage the full lifecycle in production with robust practices and technology. In order to achieve this, enterprises need a solid MLOps platform that can help accomplish both of those needs.

Business leaders and IT leaders will finally be able to cross that last mile in their AI journey to:

  • Scale AI initiatives broadly by quickly and easily embedding machine learning into existing business processes and systems across the enterprise
  • Harness and maximize investments in existing data science tools and technology and create a centralized, system of record across all teams and projects
  • Facilitate the collaboration of data science and IT/Ops teams to work together to deliver value-creating ML-powered applications
  • Decrease risk to the organization by standardizing and putting in place robust governance checks and balances, and best practices for machine learning projects in production

“Today, ML has a profound impact on a wide range of verticals such as financial services, telecommunications, healthcare, retail, education, and manufacturing. Within all of these sectors, ML is driving faster and better decisions in business-critical use cases, from marketing and sales to business intelligence, R&D, production, executive management, IT, and finance.”

Benefits of MLOps

Among many positive aspects of ML, a few topline benefits directly relate to any organization’s ability to stay relevant and grow in this tech and information-driven world. Most experts agree that the MLOps positive impacts are:

  • Rapid innovation through robust machine learning lifecycle management
  • Create reproducible workflow and models
  • Easy deployment of high precision models in any location
  • Effective management of the entire machine learning lifecycle
  • Machine learning resource management system and control

From data processing and analysis to resiliency, scalability, tracking, and auditing—when done correctly—MLOps is one of the most valuable practices an organization can have. Releases will end up with more valuable impact to users, the quality will be better, as well as performance over time.

Challenges with MLOps

Along with the benefits come the risks and challenges. Nobody said it´s gonna be easy. As exciting as ML may sound, the fact is, as this technology operations practice comes into play, there are many difficulties an organization faces that stem from how to properly combine code and data to achieve predictions. As outlined in Wikipedia, such difficulties are:

  • Deployment and automation
  • Reproducibility of models and predictions
  • Diagnostics
  • Governance and regulatory compliance
  • Scalability
  • Collaboration
  • Business uses
  • Monitoring and management

With these difficulties in mind most organizations “never make it from the prototype stage to production. A commonly cited reason for this high failure rate is the difficulty in bridging the gap between the data scientists who build and train the inference models and the IT team that maintains the infrastructure as well as the engineers who develop and deploy production-ready ML applications.”

However, with careful consideration and with knowledge of these difficulties, it is possible to reach a smooth MLOps goal with the implementation of standard practices.

Preparing for MLOps

As machine learning and AI propagate in software products and services, we need to establish best practices and tools to test, deploy, manage, and monitor ML models in real-world production. In short, with MLOps we strive to avoid “technical debt” in machine learning applications.

MLOps allows your data scientists freedom to do what they do best — find answers. You didn’t hire your data team to understand the ins and outs of your industry. You didn’t hire them to keep up with regulation. You hired them for their skills in information gleaning. Remove the barriers and let them find your answers. Take business decisions off their plates, and they can build and deploy models that get your insights more quickly.

MLOps follows a similar pattern to DevOps. The practices that drive a seamless integration between your development cycle and your overall operations process can also transform how your organization handles big data. Just like DevOps shortens production life cycles by creating better products with each iteration, MLOps drives insights you can trust and put into play more quickly.

A common architecture of an MLOps system would include

  • data science platforms where models are constructed as well as the
  • analytical engines where computations are performed, with the MLOps tool orchestrating the movement of machine learning models, data and outcomes between the systems.

Bringing ML into production does require your organization settle a few things before your model can officially be known as MLOps.

  • What are the benchmarks? — Your KPIs should be clear and measurable so that everyone is on board. Data science teams understand what’s at stake and operations personnel understand how to use insights to move forward or pivot.
  • Who is monitoring? — ML uses non-intuitive mathematical functions. The black box requires constant monitoring to ensure you’re operating within regulation and that programs are returning quality information. You may have to retrain data periodically, and determining how and when to do so needs critical collaboration between the teams involved. With an operational system in place, there shouldn’t be any confusion.
  • How are you ensuring compliance? — MLOps should have a comprehensive plan for governance to ensure your programs are auditable and to assist with explainability. If GDPR doesn’t strike fear in your soul, maybe you weren’t hit that hard with its initial introduction. Deploying ML, however, might have you afoul of all kinds of regulations designed to protect customers in the age of big data.

Choose a strong partner to get started

Deploying machine learning effectively means more than running numbers or leaving your data scientists on their own to figure out compliance and business insight. It’s crucial to take responsibility for production level ML so your operations team knows how to approach this new era of data and your data team is fully supported to do what they do best. Looking ahead to operations ensures you’re not only ahead of the machine learning curve, but your adoption is smooth and immediately insightful.

We are ready to assist you in getting your MLOps up and running.