Build Better Models with MLOps
Although still a relatively new field, the present global situation and changes have brought AI to the point where machine learning models really need to start showing their value. To achieve that, DataRobot provides a solution for organizations to build an MLOps foundation that allows data, development, and production teams to work together to successfully deploy and manage machine learning services at scale.
Machine Learning Ops – or MLOps – brings together data scientists and operations professionals to better manage the machine learning (ML) lifecycle. MLOps essentially helps bridge the inherent gap between the data teams who mainly focus on building and training machine learning models and the Operations teams who are accountable for all the services running in production, which are now starting to include machine learning services. By bridging this gap and streamlining collaboration between these teams, MLOps helps by finally seeing AI make it into production and provides measurable business results from machine learning.
A well-designed MLOps system streamlines all the steps of a machine learning model’s lifecycle, from training and deployment, through its production life, degradation, retraining, re-deployment, and to retirement and risk-managed storage.
Why Do Organizations Need MLOps?
According to a survey by NewVantage Partners, only 15 percent of leading enterprises have deployed AI capabilities into production at any scale. Many of those organizations that have taken the plunge into AI have yet to see tangible business benefits.
The challenges organizations face in deploying effective machine learning models include:
- Lack of available data science talent.
- Lack of communications between operations teams and data science teams, resulting in poor awareness of the inherent dependencies of the organization upon both of these teams to work in unison in order to achieve the goal of value realization from AI.
- Machine learning models that don’t perform as expected due to changing patterns in data, such as user or consumer data.
- Poor understanding of the characteristics and sensitivities of machine learning, resulting in justifiable concerns and huge delays, even though they could be easily mitigated.
- Overall underestimation of what it takes to ensure streamlined and risk-free deployment of models into production
MLOps helps organizations resolve these issues and streamline their path toward AI and machine learning processes that achieve real results.
The Four Pillars of MLOps
DataRobot breaks down the MLOps process into four critical areas to achieve the level of sophistication needed for real impact. These are:
The Importance of Model Deployment
A newly built machine learning model is useful only for demonstration purposes – until it’s deployed. Yet making a model operational means integrating it into an IT environment that may have been designed for something completely different. The result? Loss of stability and the ability to scale the model, reducing its effectiveness.
MLOps simplifies the integration of machine learning models with existing business systems by streamlining the transition between model production and deployment. It eliminates the friction that different platforms or languages can cause and allows data scientists to plug in their models and quickly see clean, consistent, and repeatable results.
Keeping Models Accurate with Model Monitoring
It’s essential that an organization keeps a close eye on all of their models performance during its operational life to ensure that any changes or degradation of performance are identified as early as possible and are surfaced and prioritized clearly, avoiding any business or mission-critical risk .
Effective model monitoring is vital for ensuring business continuity Advanced monitoring capabilities are not limited to traditional performance, but also include principles such as humility in AI, which gives models the ability to inform data scientists when models may be going bad, or when they’re not confident in their own predictions.
Managing a Model for Its Entire Lifecycle
The number of machine learning models in production can grow exponentially, making their management a headache. It’s labor-intensive to bring order to this process, which includes manually checking a model’s capabilities against newer models; troubleshooting models that aren’t performing well, and updating models without interrupting business continuity, all coupled with workflow approvals.
MLOps helps solve this challenge by managing the machine learning pipeline and automating key tasks, such as watching for drift, keeping audit trails for models, and managing the gating of new models via champion/challenger mechanisms. This creates a secure workflow and better management of your models as they scale.
Effective Model Governance
Machine learning models can create regulatory, compliance, and corporate risk minefields, especially after the introduction of regulations such as CCPA, EU/UK GDPR, and others. This is even a larger problem for global organizations, where the labyrinth of rules and laws becomes impenetrable.
MLOps helps companies minimize regulatory or financial risks by regularly auditing models for speed, accuracy, and drift on one hand, and governing and documenting the approval workflow and process of interference with the production machine learning environment on the other. The result is a transparent model pipeline, reduced model bias, and meeting regulatory compliance requirements.
DataRobot Can Become Your MLOps Solution
DataRobot has deep experience with MLOps and how it can benefit organizations that want to get the most from AI and machine learning. To learn more, download and read our whitepaper MLOps 101: The Foundation for Your AI Strategy. It explains the reasons to use MLOps and how it can create an organization driven by AI. It also highlights how MLOps can help different parts of a company, such as data scientists, software developers, IT operations teams, and others to finally generate the value they expect from AI for their organizations.