What does "AI transformation" mean?
“Some companies’ response to AI starts and ends with tactically building a few small projects. But the strategic question is: How will AI transform your industry's core business, and how will that change what it takes for your company to thrive?"
- Andrew Ng
In this post, we'll discuss how to successfully advance AI adoption by creating value through AI and machine learning.
To advance AI adoption is the mission of the AI Guild: Europe’s largest data practitioner community with 1000+ members, of which 200+ are leaders in the field, e.g. technical leads, business architects, Bootcamp directors, university professors, startup founders, industry CxO.
Let’s dive right in:
AI transformation should start with concrete projects, but it cannot end there
After building a few proofs-of-concept (POCs) and pilot projects, you are familiar with some of the challenges around:
Collect: Make data simple, accessible and relevant.
Organize: Create a business-ready analytics foundation.
Build: Train and evaluate models with trust and transparency.
Deploy: Operationalize models by connecting them to the existing systems and monitoring outcomes in the business.
Now it is time to leverage the acquired experience, skillset and tech to build more, easier and at a lower cost. It is not efficient to build a new data pipeline for each new use case. Many large technology companies have created their own platforms for building and deploying ML solutions:
Uber has Michelangelo
Facebook has FBLearner Flow
Google has TFX
Databricks has MLFlow
By designing and implementing such an end-to-end data system, data science teams scale use cases across the company. It allows the team to keep iterating and improving the outcomes as the technology, market, and customer expectations keep changing.
Just grand strategy (AI by PowerPoint), and just implementation (hacking models) won’t do. AI transformation requires the fusion of strategy and implementation for a virtuous circle at scale...
The best way to connect strategy and implementation is by building the platform that drives your future
The benefits are the ease of model training and deployment which leads to deployment and training at scale. It allows for monitoring data governance, quality, and compliance because the whole pipeline can be visualized.
There are a variety of options for tooling and frameworks, and the MLOps space keeps evolving. If you're looking for an extensive and insightful read, check out this resource from Chip Huyen, where she surveyed tools and analyzed trends. We also recommend subscribing to the MLOps Roundup newsletter to receive straight to your inbox the best articles, papers, and news curated by the experts Nihit Desai and Rishabh Bhargava.
What #datalift achieves
We understand the current challenges and can assess data projects for technical quality, review the architecture, and determine the use case value.
Across more than ten industries, from leading corporates to leading startups, and on to new firms, we have heard from those having use cases in production. With a loyal audience of practitioners, of which more than half have roles as Seniors, Leads, Directors, and CxO, the #datalift event series from No 1 to 5 has become the key forum for exchange on what works and what is best practice.
Scaling solutions? Ask the AI Guild to help secure the quality and productivity of your deployed solutions. Book your conversation with us.