Like a racetrack, AI scaling is not a straight line. Your data and AI journey comprises various turns: sweeping turns, hairpins, chicanes, etc. Like in car racing, navigating these turns effectively and exiting the turn at full throttle is your secret to winning the AI race. But, often, doing it right takes much time, and you never have enough time. This article reviews three key steps to achieve more with less and accelerate your time to value with AI.
Step #1: Remove Roadblocks With Better MLOps and Take Every Turn Without a Hitch
The road to AI scale is challenging, as detailed in my prior blog. Many roadblocks can slow you down: detecting model drift during production, dealing with low-quality data, managing performance and accuracy concerns, and slow retraining, to name a few. These obstacles must be scrutinized by technical leaders when industrializing AI processes.
According to McKinsey in “The State of AI in 2022,” taking a whole lifecycle approach to develop and deploy AI models is the primary differentiator that separates high performers from the rest. This MLOps lifecycle approach must provide the necessary tools and capabilities to address the specific needs of multi-skilled data and AI teams.
Considering your data and AI projects under a virtual lifecycle is your best option to avoid crashing at the slightest chicane. For example, Dataiku can solve the time-consuming process of monitoring model performance. Model evaluation in Dataiku provides detailed, historized performance charts and metrics to help inform data scientists and IT operators whether to retrain or refactor a live model. Dataiku also helps to reduce manual oversight by alerting operators with early warning of data anomalies or degrading performance. For ITOps, it’s a perfect way to smooth your process in your industrialization strategy. More on this here.
Step #2: Speeding Data Democratization Without Going Off the Track
Today, making the best possible use of reliable data remains critical for businesses that want to turn data into a competitive advantage.
To create that advantage, building and easily sharing data assets is necessary to encourage rapid adoption by business users. But there is always a risk in sharing data products. Putting data in the hands of business users requires control. For this, permissions must be defined and efficiently managed at the project but also at the asset level to allow a simplified and secure use of the data objects.
Collaboration and asset reuse have always been two critical benefits of Dataiku’s platform. Since our Dataiku 11 release, new quick sharing and managed sharing mechanisms have been enhanced so that teams can strike the right balance between control and discoverability. Dataiku projects can be set as private or limited access, and teammates can request access to projects and assets for reuse elsewhere. This means more visibility and awareness of other teammates’ work, leading to increased opportunities for asset reuse with less time spent by project owners.
Step #3: Get a Head Start Using Solution Accelerators and Cross the Finish Line First
There are several ways to accelerate time to value for AI projects. You can outsource your efforts and have managed point solutions, or you can hire more talent to develop your fully customized in-house solutions. Outsourcing can be costly and not very effective over time. Onboarding new talent and getting them up to speed can consume time and money. And sometimes, you run out of both before you can create value.
For companies that want to do more faster, without spending more, having a wide array of industry and business accelerators is a huge plus when choosing a data and AI platform. Think about a brand that directly sells its products online. Imagine that this brand is losing market share without being able to retain your customers. They would like to offer a potential buyer what similar users are buying on your site. Digital marketers may be considering a recommendation system. But they don’t have time to wait and must deliver it quickly.
Dataiku's product recommendation solution offers a ready-to-use solution for that. Using collaborative filtering and machine learning in the ready-to-use solution, the brand could quickly improve product discovery, increase customer engagement, and develop incremental revenue with minimized time & resources investment.
This is a retail example, and there are many others. With more than twenty industry solutions, and more on the way, Dataiku has leveraged its extensive experience to create a comprehensive collection of off-the-shelf accelerators. These resources have been designed to significantly reduce development time and provide inspiration for teams tackling time-to-value challenges.
Secure Your Spot at the Front of the Grid
In the first blog post in this series, we saw how essential robustness and agility are for a data and AI platform while keeping it universal to onboard business users. In this post, we have seen why speed is key when responding to a constantly changing world. Streamlining your processes and relying on collaborative features allows you to intensify usage and speed up your projects, and accelerators help you stay ahead while ramping up your teams to tackle new industry-specific use cases quickly.
During the scale-up phase, you also need to manage projects effectively and ensure that they are deployed with full security, transparency, and compliance with regulatory constraints. And this is what we will discuss in our third upcoming blog post.