This article was written by Sylvain Ferrandiz and originally appeared on the DataRobot Blog here: https://www.datarobot.com/blog/keys-to-ai-success-for-it-staff/
When your employer adopts AI solutions, your work may be simplified in the long run, but implementing the new technologies may require some initial effort.
To adapt to the changes, follow these guidelines.
Declutter the Tech Stack: Adopt an End-to-End Solution
Instead of using separate tools that were not designed to work together, focus on creating a single ecosystem of technology infrastructure. This approach gives freedom to move its AI artifacts around, regardless of whether they are hosted on a major cloud platform or its own on-premise infrastructure.
Having an end-to-end platform makes daily tasks easier to accomplish. It also:
- Allows your staff to concentrate on strategic work.
- Standardizes data management and other aspects of the AI lifecycle.
- Requires learning a single technical solution.
- Enables support issues to be addressed more quickly.
Implement MLOps Tools
Machine learning operations (MLOps) solutions allow all models to be monitored from a central location, regardless of where they are hosted or deployed.
These tools can resolve common model management problems:
Challenge 1: Slow Iteration Speed
Manual processes cannot keep up with the speed and scale of the machine learning lifecycle, as it evolves constantly.
Solution: Because MLOps tools operate from a central location, they enable IT staff to easily handle the constant flow of model deployment and monitoring.
Challenge 2: Different Training and Production Architectures
Organizations often have multiple training tools, and a lengthy compute lifecycle.
Solution: MLOps allows models to be put into production in short compute bursts that accommodate many different users.
Challenge 3: Heterogenous Tooling and Dependencies
Typical IT departments work with dozens of evolving language and framework combinations and hardware modifications.
Solution: Flexible MLOps systems allow staff to manage constant changes in dependencies and languages.
Challenge 4: Factor of Composability
IT routinely operates related software components that have been selected and assembled in various combinations to satisfy user requirements.
Solution: MLOps applications are elastic and stateless, so they work efficiently in a constantly changing landscape.
Challenge 5: Auditability and Governance Requirements
Traceability requirements require the creation of records that show who called out what data, when, and why.
Solution: MLOps provides version control, automated documentation, and lineage tracking for all production models.
Challenge 6: Reusability Concerns
Models often exist only on laptops or local servers; incompatibility can result from the use of multiple languages and frameworks.
Solution: Because MLOps allows model reuse, data scientists do not have to create the same models over and over, and the business can package, control, and scale them.
Most organizations find that the best MLOps solution is an external system that provides a single environment for continuous integration and deployment of AI projects.
Deliver Continuous Learning
Businesses that embrace change succeed. But when the marketplace shifts — and your data along with it — what processes can you put in place to adapt quickly? The answer is continuous learning, a fundamental component of efficient AI solutions.
Continuous learning requires:
- Adopting automated strategies that keep production models at peak performance.
- Refreshing models according to the business schedule or signs of data drift.
- Constantly creating and testing new challenger models.
Models need to be simplified by constant iteration and experimentation. Although pre-training and tuning before deployment are important, fine tuning after deployment increases accuracy.
When your business has a backlog of use cases, its data scientists need to spend hours working on each problem. But a high-quality automated machine learning (AutoML) tool capable of continuous learning can break this cycle, allowing models to go live without wasted time.
With an AutoML system working in the background, you can run experimental challenger models continuously after deployment. Thus, you can modify a model when needed without changing the pipeline that feeds into it — providing a data science improvement without any investment in data engineering.