Data science and machine learning are becoming the critical building blocks for data-driven organisations. Now accelerating ML lifecycle management and taking the models from prototyping to production has become increasingly important. Another factor that has led to the rise of ML lifecycle solutions is the significant increase in expectations from engineering teams to meet the varied demand to develop and scale ML capabilities.
In a recent talk at Strata Data Conference, Hope Wang from Intuit defined the machine learning platform as not just being the sum of its parts. He said that the key to ML was how it supports the model lifecycle end to end. This includes data discovery, feature engineering, iterative model development, model training, and model scoring (batch and online). The management of artefacts, their associations, and deployment across various platform components is vital. ML lifecycle management has started to play a key role in organisations that face different challenges when it comes to developing machine learning artificial intelligence algorithms and putting ML in production.
Today, large enterprises and startups face a clutch of different challenges when it comes to developing ML, AI algorithms and putting these models into production. According to one tech writer, ML development is an experimental and exploratory process, whereas deployment demands consistent results that are secure and well-managed. A lot of engineering organisations are expected to develop and scale ML capabilities.
Importance Of Machine Learning Life Cycle Management
It is important because it delineates the role of every person in a company in data science initiatives, ranging from business to engineering. It takes each and every project from inception to completion and gives a high-level perspective of how an entire data science project should be structured in order to result in real, practical business value. Failing to accurately execute on any one of these steps will either result in models with no practical value or that provide actively misleading insights.
While there are a number of mature technologies that support each phase of this lifecycle, there are limited solutions available that tie these components together into a cohesive ML platform. To support the lifecycle of a model, you must be able to manage the various ML-related artefacts and their associations and automate deployment. A lifecycle management service built for this purpose should be leveraged for storage, versioning, visualising (including associations), and deployment of artefacts.
Of late, there has been a spurt in enterprise solutions which operationalise ML lifecycle management tasks. For example, US-based startup Quickpath allows data science teams to operationalise ML across the organisation and streamline the path to production. According to a company statement, the Quickpath ML OPS and Engineering Platform is the only comprehensive production data science platform present in the market today.
Key Features Of ML Platform
- The platform should support model development in different programming languages, and language and package versions should be configured specific to a model.
- Another key feature is the connection between various artefacts and platforms — the data and datasets: source data and feature data, training datasets, and scoring result sets; code — notebook code, model code, deployment code and lastly model-specific environments and platforms, for example, developing and training platforms
- The components of the ML platform should associate and interact.
Another company, DataRobot also provides automated ML platform that streamlines the ML life cycle by simplifying the most complicated, time-consuming steps with automation. The platform makes data exploration and model building easier and more accessible, allowing those who understand the business problem behind the data science project to rapidly build and test dozens of models in a fraction of the time it would take using traditional methods.
With ML gaining more traction in businesses, a development lifecycle that supports learning models for building custom ML algorithms and applications has become very crucial. Hence, it is important for data-driven organisations to choose an ML platform that provides interoperability with other ML frameworks.