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Every company is becoming an AI company, and engineers are at the forefront of helping organizations make this transition. Engineering teams are increasingly being asked to incorporate machine learning into their product roadmaps and monthly OKRs to enhance their products. This includes everything from implementing personalized experiences and fraud detection systems to, more recently, natural language interfaces leveraging large language models.
AI’s Dilemma for Engineering Teams
Despite the growing list of ML expectations and roadmap items, most product engineering teams face some key challenges when building AI applications.
- Lack of the right data science resources to help you develop custom ML models quickly in-house
- Existing low-level ML frameworks are too complex for immediate adoption. Writing hundreds of lines of TensorFlow code for a classification task is no easy task for a machine learning novice.
- Training a distributed ML pipeline requires deep infrastructure knowledge, and model training and deployment can take months.
As a result, engineering teams remain hobbled in their AI efforts. Q1 targets become Q2 targets and finally ship in Q3.
Removing roadblocks for engineers with declarative ML
A new generation of declarative machine learning tools, first developed at Uber, Apple, and Meta, is harnessing this power by making AI accessible to engineering teams (and anyone interested in ML for that matter). We aim to transform relationships. Declarative ML systems simplify building and customizing models with a configuration-driven approach grounded in the same engineering best practices that Kubernetes revolutionized infrastructure management.
Instead of writing hundreds of lines of low-level ML code, you simply specify your model’s inputs (features) and outputs (values ​​you want to predict) in a YAML file, and the framework provides an easy-to-customize recommended ML pipeline. increase. These features enable developers to build powerful, production-grade AI systems for practical applications in minutes. Ludwig, originally developed at Uber, has over 9,000 stars in Git and is the most popular open source declarative ML framework.
Get started building AI applications easily with Declarative ML
Join us for upcoming webinars and live demos to learn how to get started with declarative ML with free trials of open source Ludwig and Predibase. In this session you will learn:
- About Declarative ML Systems (Including) Uber’s Open Source Ludwig
- How to build and customize ML models and LLMs for any use case in less than 15 lines of YAML
- How to use Ludwig and Predibase to quickly train, iterate and deploy a multimodal model for bot detection and how to access a free trial!
