Building a predictive model to forecast the future from historical data is standard practice for today’s businesses. But deploying, scaling and managing these models is far from simple.

Each ML solution depends on an algorithm (code) and a set of data used to develop and train the algorithm. For this reason, building ML solutions is different to other types of software development. 

Enter MLOps, or machine learning operations, a set of processes that help organisations to develop, deploy and monitor ML models at scale by applying best practices to infrastructure, code and data. 

MLOps is a relatively new idea but one that has been adopted by many organisations – the market for MLOps solutions is expected to reach $4 billion by 2025. At Equal Experts, we have been involved in developing and deploying AI and ML for a number of applications including to: 

• Assess cyber risk 
• Evaluate financial risk 
• Improve search recommendations for retail websites 
• Improve logistics and supply chains 

Key Terms used in MLOps

If you’re new to MLOps there are several important terms to be aware of:

• Machine learning (ML) – a subset of AI that involves training algorithms with data rather than developing hand-crafted algorithms. A machine learning solution uses  a data set to train an algorithm, typically training a classifier that says what type of thing  this data is (e.g. this picture is of a dog ); a regressor, which estimates  a value (e.g. the price of this house is £400,000.) or an unsupervised  model, such as generative ones  which can be used to write novel text (such as song lyrics).  
• Model – In machine learning a model is the result of training an algorithm with data, which maps a defined set of  inputs to outputs.  
• Algorithm – we use this term more or less interchangeably with model. (There are some subtle differences, but they’re not important and using the term ‘algorithm’ prevents confusion with the standard software engineering use of the term ‘data model’ – which is a definition of the data entities, fields, relationships etc  for a given domain, that is used to define database structures among other things.)
• Ground-truth data – a machine-learning solution usually needs a data set that contains the input data (e.g. pictures) along with the associated answers (e.g. this picture is of a dog, this one is of a cat)  – this is the  ‘ground-truth’.
• Labelled data – means the same as ground-truth data. 

How does MLOps work? 

We talk about MLOps as a set of processes that help data scientists to develop consistent, scalable ML models, and monitor their performance. To create and use these algorithms, you will usually follow these steps: 

Initial development of the algorithm – Developing a model is the first step in machine learning. Data scientists will identify or create ‘ground truth’ data sets and explore them. They will build and evaluate prototypes of the models, trying out different core algorithms and data transformations  until they arrive at  one which meets the business need.

Integrate/deploy the model – once the model has been built, it must be integrated into the business. This can be done in various ways depending on the consuming service. In modern architecture, models are commonly implemented as a standalone microservice and models are deployed by copying an approved version of the model into an operational environment. 

Monitor performance – All ML models need to be monitored to ensure they’re running and meeting demand, but also that the results of the model are accurate and reliable.

Update model – over time, models must be retrained to reflect new data, or improvements to the model. In this case, it’s important to maintain version control and to direct downstream services to the new model.  

Operationalising Machine Learning 

Our MLOps playbook brings together our experiences working with algorithm developers to build ML solutions. It provides a comprehensive overview of what you need to consider when providing the architecture, tools and infrastructure to support data scientists and to integrate their outputs into the business.

Download the playbook for expert guidance on how your organisation can attain  the promised business value from algorithms by providing engineering to support algorithm development, and by integrating ML more effectively into your business processes. You’ll find helpful advice on how to:

• Collect data that drives machine learning, and make that available to data scientists 
• Integrate algorithms into your everyday business 
• Configuration control, deploy and monitor deployed algorithms 
• Test and monitor the algorithms  

View our online version or download a pdf here.