Companies of all sizes and in all industries are developing ways to harness the power of big data for better decision-making. To provide valuable insights and meet expectations, data science teams have long turned to predictive analytics – or using historical data to model a problem and uncover the key factors that generated specific outcomes in the past to make predictions about the future. Predictive analytics has been around for years; however, prior to machine learning, the technology was not easy to adopt or scale in real-time.
Machine learning is modernizing predictive analytics, providing data scientists with the ability to augment their efforts with more real-time insights. And thanks to hybrid cloud infrastructure opportunities, it’s now possible to embed and scale predictive analytics in almost any business application quickly and efficiently.
The ability to process larger quantities of data in real-time results in more accurate predictions, and therefore, better business decisions. However, modernizing predictive analytics is not without its challenges. Here are a few ways companies can modernize the deployment of their legacy predictive models, and the pros and cons of these popular approaches.
Rebuild models on a new framework
Organizations can implement modern predictive models by taking existing training and validation data sets and rebuilding them within the latest technology frameworks. For instance, companies can rebuild their predictive models using a similar algorithm (for instance, using H2O.ai or scikit-learn) and compare it to the legacy model results.
The primary advantages of simply rebuilding predictive models in a new framework are that organizations can reuse existing datasets, code, and previously successful algorithms. Rebuilding the model in a new framework is a relatively straightforward process, and allows companies to understand the complete modeling lifecycle as its implemented – without much additional effort.
One disadvantage of rebuilding predictive analytics models in a new framework is that it’s difficult to know right away if the new model will outperform the legacy model once in production. Modernizing predictive analytic models with a new framework is a good opportunity to use more data to drive the models. However, if companies simply reuse the same training and validation datasets, then they might miss the chance to update their datasets with new data sources, features, algorithms, among other things.
Rebuilding predictive analytics models on a new framework is also just the first step. The next step is to bring the models in the production environment and deploy them, which can be a time-consuming, complex process depending on the framework and the production platform being used. Overall, the ROI of deploying an old model on a new framework can be low.
Deploy models in a cloud environment
An in-house, private environment is necessary when there are portions of an organization’s data and infrastructure that need to remain behind the corporate firewall due to data privacy regulations, industry standards, etc. However, many enterprises are turning to a cloud environment, or hybrid cloud environment, to deploy predictive models. This approach comes with its own set of pros and cons.
The big data industry is consolidating around three public cloud providers – Google Cloud Platform, Microsoft Azure, and Amazon Web Services. As a result, enterprises are migrating to these services for more flexibility to choose and customize their environments and deploy predictive models and applications more quickly. Moving existing models to a cloud environment has many advantages, among them the ability to accelerate testing on an established platform and more comprehensive administrative tools.
One challenge of transitioning to a cloud environment is that many of the legacy predictive analytics providers have yet to exploit the benefits of the cloud environment, making it difficult for data science teams to introduce new algorithms, frameworks, and applications in this new environment. Companies will also need to address the issues of where the data will live, and who will monitor the models and applications when everything has been migrated to a third-party cloud environment.
Public cloud environments are becoming the preferred analytics platform across a variety of industries; however, it’s important to consider the ROI and true business challenges and benefits of deploying existing models in another environment before making any changes.
Deploy models with Quickpath
Moving predictive models into the production environment is a key challenge for many companies. The models need to be injected into existing systems and executed efficiently and cost-effectively without disrupting business activities. Quickpath works seamlessly with existing data analytics infrastructures so companies can get the most out of their data and analytic tools of choice regardless of where they live.
Predictive analytics modernization typically includes migrating on-premise systems to cloud platforms, bringing together historically disparate data sources, moving from batch to real-time or streaming data ingestion, and setting up a data virtualization layer for a single source of truth for reporting and model development purposes.
With Quickpath, data science teams can go from legacy analytics systems to deployment on a modern, efficient, and scalable platform. The legacy models can be exported to the Quickpath platform without the need to rebuild them. They can be run within the new infrastructure and even in parallel with modern machine learning models for champion-challenger testing, ensemble models, or any AI-based decision-making. Essentially, companies get the best of the previous two approaches to modernization by not having to rebuild legacy models or moving all the operational data into a new environment. Quickpath bridges these environments while providing these capabilities and leveraging the latest data solutions for modern data science. Companies can do modernization on their timeline and get to focus on the efforts that provide the highest ROI.
The Quickpath platform has four key capabilities required for taking AI and predictive models to production, including:
Universal Model Management - A central repository of all models and decisions. Businesses can track every model, outcome, and data used to make a decision, as well as access reporting and analytics, and proactive drift and anomaly detection.
Real-Time Decisions - Deploy models, data, and rules in real-time to make recommendations that drive the most business value.
Integration Capabilities - Integration of the people, process, and technology that support predictive analytics, and an open architecture to connect and integrate with the relevant systems.
Elastic Runtime Environment - Configure and scale elastic runtime environments to run anywhere.
The big data analytics market is changing drastically. In fact, the market is expected to grow at a Compound Annual Growth Rate (CAGR) of 29.7% and reach $40.6 billion by 2023. The modernization of predictive analytics means organizations are finally moving out of the experimentation stage and not only making faster, smarter decisions but achieving real business value from their deployments as well.