In a recent Dataiku Product Days session, Padmashree Shagrithaya, EVP of Analytics and AI at Capgemini, shared with us the reasons why systematizing your organization’s approach to scaling AI is so important. In this blog, we are excited to share her insights, including a look into why MLOps is the puzzle piece you’ve been missing all along.
Are Your Models Really Ready to Roll?
Many organizations are already working on building out AI models, but that does not mean they already have it all figured out. In order to ensure the long-term success of scaling AI, it’s important for organizations to apply AI in a systematized manner.
When embedding AI across all of your processes, a certain degree of chaos is inevitably created. It’s your organization’s approach to this uncertainty and mobility that can make or break the success of your machine learning (ML) projects.
Every industry has a multitude of problems that they can potentially solve through ML algorithms and, as they expand to cover each of these many areas, the complexity of the model mechanisms increases in correlation. One example of this is when the seemingly simple ML project of demand forecasting branches out into many models relative to particular geographies or specific supply chain questions which may arise.
This is all to say that organizations often find themselves struggling to keep pace with the projects that pop up beyond the initial problem they sought to solve. Is your approach to AI one that is able to support the various ML projects that you will eventually want or need to manage?
Sink or Swim
As we touched on, it’s easy for organizations to get overwhelmed in the chaos and possibilities in front of them when it comes to scaling AI, which is why bringing a method to the madness is critical. For a good starting point, let’s look into the key areas organizations should pay attention to when adopting AI at scale:
- Breaking silos between data scientists, ML engineers, and business teams
- Governing ML models to minimize risk and ensure regulatory compliance
- Retraining, recalibrating, and redeploying ML models
- Reducing time to market for POC to production
- Improving ROI through a fully integrated AI platform
These aren’t exactly quick or easy fixes and a successful strategy starts with organizations’ executives asking key questions to determine readiness and investigating internal processes to identify the pieces of the puzzle they might be missing. What can be systematized that will allow the AI initiatives to swim instead of sink?
The Piece of the Puzzle You Might Be Missing: MLOps
Bringing it back to the basics, the things that teams should be looking for to address the key challenges associated with AI at scale are effective transitions from experimentation to production, efficient model deployment, and active quality assurance. These components directly relate to the core process of MLOps. A process that very few organizations are dedicating adequate time and resources to.
To clarify, MLOps is the process of taking an experimental ML model into a production system, based on the principles of DevOps. Continuous integration and monitoring are its key dimensions. According to Shagrithaya, only 15% of organizations believe that their IT operations teams completely understand the requirement to deploy ML models, only 6% of companies feel that their MLOps capabilities are mature or very mature, and 42% of companies indicated low collaboration between their data science and application development teams.
Here are three reasons those percentages should be worrisome if you want to scale AI:
- MLOps simplifies model deployment by streamlining the processes between modeling and production deployments.
- MLOps allows production teams to monitor models in ways specific to ML and is able to proactively monitor data drift, feature importance, and model accuracy issues.
- MLOps enables companies to minimize corporate and legal risks, maintain a transparent production model management pipeline, and minimize and even eliminate model bias.
MLOps is the key that leverages people, platforms, and processes to operationalize the ML lifecycle by automating many of the steps required to deploy and manage models in production environments. It ensures collaboration across the entire AI team to share models, access the right data, initiate handoffs, provide visibility into production, and act quickly when issues do arise.
What to Remember
ML transforms how companies view insights and deliver business value, yet it’s often complicated to put models into enterprise-wide production when proper systemization isn’t present. So, at the end of the day, what organizations should remember is that they should aim to create ML models which are reusable, responsive, robust, and responsible through appropriate MLOps measures.
- Reusable: Integrate the AI/ML models into the business processes and reuse templates, pipelines, and use cases from existing ecosystems across new functions
- Responsive: Prioritize business needs by automating out bureaucracy, and activating conversations with data for faster decision making
- Robust: Enable continuous training of models with MLOps and develop a robust deployment framework
- Responsible: Understand and monitor your data, models and AI/ML processes to build trusted and transparent solutions
With effective MLOps, your organization adopts a disciplined framework that allows you to scale ML in the enterprise efficiently and steadily.