These are the benefits of AutoML:
- Efficiency improvement: The dramatic Increased productivity is one of the strongest arguments in favor of using AutoML tools. The more efficient you can be with your machine learning time, the more money you will save.
- ML to Scale: Instead of devoting all your time to repetitive modeling activities, you can use AutoML to focus on scaling up your machine learning applications. This allows you to address issues swiftly and effectively throughout your whole firm.
- Bridges Skill Gaps: This gives you a chance to fill vacuums in your organization’s skill set. Given this, outsourcing your AI solutions, and making a one-time investment in an AutoML tool frequently makes more sense financially and logically than keeping an in-house staff of data scientists.
- Promoting AI: AutoML solutions assist business analysts and subject matter experts build guardrail-safe models. These roles no longer need model-building data scientists. AutoML can make data science more approachable and help mainstream AI.
- Decrease in time to market: Automating certain model development procedures may boost efficiency and reduce market time. AutoML models are not as excellent as manually customized ones, but they’re no worse than basic ML models. It is also crucial to establish a footing swiftly and decisively while confronting severe competition.
There are a few limitations of AutoML:
- Flexibility: AutoML frameworks can handle most datasets, however, this generalization may leave out unusual datasets. Automated Machine Learning works well in most circumstances, but for other challenges, it may lack the accuracy and persistence of a human method.
- Business challenges: The goals for the present AutoML system optimization are set. Realistic challenges often include many goals, such as the requirement to draw fine distinctions between cost and decision-making. People have few opportunities to appraise this kind of multi-objective research adequately before the findings are known.
- Processing Capacity: AutoML tests almost every model before choosing the best method. This process utilizes several computing resources. In the manual version, the data scientist first eliminates incorrect algorithms.
We have covered the AutoML section of GCP in detail for images and text data in this chapter. We also talked about the benefits and limitations of AutoML. In the next chapter, we will start working on the custom models.
- Can we have multiple objectives for model training in AutoML?
- Can pre-built models of GCP used for all business use cases?
- When do we choose AutoML over custom models?