Automating Model Training with MLOps: Best Practices and Strategies

Preparing the data, analysing it, and then training the model is referred to as the MLOps cycle in the context of the model training pipeline. The MLOps pipeline’s model training is frequently automated using AutoML features built into this iterative or interactive model.

What is MLOps pipeline automation?

In an MLOps automated training model, pipeline automation entails the execution of model training continuously, and model retraining is triggered anytime fresh data becomes available. Steps for validating data and models are also included in this degree of automation.

What is Automated Machine Learning?

A major change in how businesses of all sizes handle machine learning and data science has been brought about by MLOps Automatic machine learning (AutoML). It takes a lot of time, resources, and effort to apply conventional machine-learning techniques to actual business challenges. It calls for specialists from a variety of fields, including data scientists, who are already among the most in-demand workers.

By applying methodical operations to unstructured data and choosing models that extract the most pertinent information from the data—often referred to as “the signal in the noise”—automated machine learning alters this and makes it simpler to construct and utilise machine learning models in the real world. Automated machine learning applies the industry’s best practices for machine learning to create a successful MLOps Pipeline and increase data science accessibility throughout the enterprise. refers to the cycle of gathering data, analysing it, and then training an AI model. In order to automate model training across the MLOps pipeline, this iterative or interactive model frequently has AutoML features.

Why is Automated Machine Learning Important?

It is a lot to expect of one organisation, much alone one data scientist, to manually build a machine learning model because it is a multi-step process that calls for domain knowledge, mathematical experience, and computer science abilities (provided you can hire and retain one). In addition, there are several potentials for human error and prejudice, which reduces the model’s accuracy and diminishes whatever insights it may provide. Automatic machine learning enables businesses to exploit data scientists’ pre-built expertise without investing time and money in building those skills themselves, increasing the return on investment for data science programmes while shortening the time it takes to realise value.

Automated machine learning makes it possible for companies in every industry to use machine learning and AI technology, which was previously only accessible to businesses with enormous resources. These industries include healthcare, financial markets, fintech, banking, the public sector, marketing, retail, sports, manufacturing, and more. Automated machine learning enables business users to easily apply machine learning solutions, freeing up an organization’s data scientists to work on more challenging challenges by automating the majority of the modelling processes required to construct and deploy machine learning models.

What are the steps involved in Automated Model Training?

Following the establishment of the success criteria and the business use case definition in any MLOps project, the following stages are involved in getting an ML model into production. These actions can be carried out manually or automatically using a pipeline.

• Extraction of data For the ML work, you choose and incorporate the pertinent data from several data sources.
• Data analysis: To comprehend the data that is accessible for creating the ML model, you undertake exploratory data analysis (EDA). The results of this method are as follows:
• Recognizing the data structure and the traits the model anticipates by determining the feature engineering and data preparation required for the model.
• The data is ready for the ML job after being prepped. Data cleaning, which entails dividing the data into training, validation, and test sets, is a part of this preparation. Also, you incorporate feature engineering and data transformations into the model that completes the intended job. The data split in the ready-to-use format is the step’s output.
• Model training: Using the given data and numerous techniques, the data scientist trains several ML models. To acquire the best-performing ML model, you also subject the implemented algorithms to hyperparameter adjustment. This phase results in a trained model.
• Evaluation of the model: The model’s quality is assessed using a holdout test set. A set of measures for evaluating the model’s quality are the result of this stage.
• Model validation verifies that the model is suitable for deployment and that its prediction performance exceeds a predetermined baseline.
• Serving the model: To provide predictions, the verified model is delivered to a target environment. There are several possible deployments for this one:
  1. Online forecasts are served via microservices with a REST API.
  2. a mobile or edge device with an integrated model.
  3. a component of the batch prediction system.

• Model monitoring: To possibly start a new iteration of the ML process, the model’s predicted performance is tracked.

The degree of automation of these phases determines the ML process’ maturity, which is a reflection of how quickly new models can be trained using new data or with iterative implementations.

How do you leverage MLOps and the power of automation for model training in 2023?

The road for today’s data-driven businesses starts with strategic knowledge and implementation of AI/ML. Before beginning the MLOps journey, company executives must assess the organisational infrastructures, goals, and pain areas. Companies can use the step-by-step instructions in the accompanying document to successfully automate MLOps.

• Using experimental coding to build a practical model: Most of the development and deployment phases of the ML model will initially remain manual after the successful adoption of ML and application to the current use cases. Engineers and data scientists start building the model, which will later be used as a prediction service. The data professionals first manually control script-driven and interactive procedures, evaluating, analysing, and building experimental codes to produce a practical model. At this point, performance evaluation and CI/CD are not given much attention. The use of a trained model as a prediction service is the main topic.
• Automation of the data pipeline comes into focus as the MLOps journey develops and a model is built. As data collection, analysis, and validation are currently automated, continuous model training leads to continuous delivery. With the scope of implementing their results in the production setting, experiments move more quickly. The unification of DevOps and the modularization of pipelines’ and components’ codes make them repeatable and independent in the runtime environment. Prediction services for new models are continuously delivered since model deployment is automated. The deployed training pipeline as a whole automatically and constantly provides the trained model. Data and model validation, a library of features, metadata management, and ML pipeline triggers are some further elements of this MLOps level.
• Transforming the pipeline into a production setting: The CI/CD system must be smoothly automated in order for the ML pipeline to be applied to the production environment with dependable and continuous updates. The creation, testing, and deployment of new pipeline components in production may be completed quickly and easily with the help of a lightning-fast and automated CI/CD system that allows data professionals to generate newer ideas about model design, feature development, and hyperparameters. Continuous experimentation with the ML algorithms is made possible by the automated CI/CD of the ML pipeline, which later helps with the creation of source codes. New components are offered through continuous pipeline integration and delivery in the production environment, ensuring newer installations. Automatic triggers aid in putting the pipeline into production and continuously implementing the environment’s taught model. The model’s real-time performance is then tracked, and incremental measures may be performed based on data-driven insights.

MLOps will be a crucial facilitator of businesses’ future efforts in data analytics. As they work to unlock commercial value at scale, strategic AI/ML initiatives, the hiring of talented and imaginative data scientists and ML engineers, and innovation-mindedness will be fundamental elements of their journeys.

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Payal Paranjape

Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.