What is the problem? In the realm of ML and AI, saturation is pervasive. Countless articles and products flood the space, accompanied by an array of terms often used interchangeably. While the traditional methodology of defining an analytical approach, selecting libraries, crafting a model, executing it, evaluating results, fine-tuning, and collaborating with ML teams for production may be established, its applicability across all businesses is questionable. What’s required for most small businesses are readily available AI & ML solutions tailored to address the specific business problems at hand.
What is the problem? We can utilize Google’s AI/ML framework and the array of AI/ML development options available on Google Cloud.
Google Cloud offers solutions tailored to various needs, including ease of use, skill requirements, budget considerations, and specific business objectives.
Pre-Trained APIs are capable of handling various data types such as tabular, image, text, audio, and video, without any specific training data size requirement and with minimal ML and coding expertise. However, the limitation of these APIs lies in their inability to fine-tune the model’s hyperparameters.
BigQuery ML is designed to handle tabular data with moderate training data requirements and requires a medium level of ML and coding expertise. Hyperparameters can be fine-tuned using SQL syntax, and the training time for the model can be customized. For further insights on leveraging BigQuery ML, you can refer to my earlier article on Rapid Model Creation with BigQuery ML available here
AutoML is versatile, accommodating tabular data, images, text, and video, with a requirement for small to medium-sized datasets and minimal coding expertise. Users can customize the training time for the model to suit their needs.
AutoML streamlines the data preparation process by automating various tasks. For instance, it can convert numbers, date-time values, text, categories, arrays of categories, and nested fields into specific data formats, enhancing efficiency and ease of use.
Easily search models and fine-tune parameters using Neural Architecture Search and Transfer Learning within AutoML.
AutoML encompasses a wide array of pre-trained models, which can serve as foundational models for addressing new business challenges. For instance, LLMs (general-purpose language models) are adept at tasks such as text classification, question answering, document summarization, and text generation, offering versatile solutions tailored to specific needs.
Transfer Learning proves advantageous for businesses with limited datasets, allowing them to leverage prebuilt models trained on similar but larger datasets.
With Neural Architecture Search, users can efficiently search for and identify optimal models tailored to their needs. AutoML conducts thorough evaluations and comparisons of different architectural models, automatically fine-tuning parameters to align with the dataset. For instance, users can explore multiple advanced ML models, ensuring precise adjustments for their data.
AutoML relies not on a single model, but on a selection of the top-performing models, typically around 10. Additionally, AutoML streamlines the ML pipeline, automating processes from feature engineering to hyperparameter tuning and model ensemble.
AutoML offers a no-code solution with a user-friendly interface for building ML models, enhancing accessibility and ease of use.
Vertex AI offers an end-to-end ML Pipeline, integrating all the aforementioned options seamlessly. This comprehensive pipeline facilitates data preparation, model creation, deployment, and management. Users can conveniently upload data from various sources such as Cloud Storage, BigQuery, or a local machine. Additionally, users can generate features, refine them, and share them with other users via the feature store. Once the data is prepared and features are refined, users can experiment with different models, adjust hyperparameters, and set up pipelines for production deployment.
Vertex AI includes preconfigured products such as AutoML and Custom Training, along with offerings in Generative AI, enhancing its versatility and usability.
The spread of ML and AI solutions presents both opportunities and challenges for businesses. While traditional approaches may not always suffice in today’s saturated landscape, the key lies in leveraging tailored solutions. Google Cloud’s diverse array of AI/ML tools, from Pre-Trained APIs to AutoML and BigQuery ML, offers adaptable frameworks suited to varied needs and expertise levels. Vertex AI’s comprehensive end-to-end ML Pipeline streamlines the entire process, providing a seamless experience from data preparation to model deployment. By embracing these advanced technologies and solutions, even small businesses can navigate the complexities of AI and ML more effectively, driving innovation and achieving their goals with greater efficiency and precision.
*Generative AI is not included in these options. I will dedicate a separate article for Generative AI.
Linkedin Article: https://www.linkedin.com/pulse/ai-ml-development-options-google-console-neal-akyildirim-8whee/