- Data analysts and other business professionals can skip major coding hurdles with no-code or low-code platforms.
- Platforms like Google AutoML, H2O.ai, and Azure AutoML automate the training process, including feature selection, hyperparameter tuning, and model evaluation.
Crafting your own AI model offers a host of benefits. Data analysts can tailor predictions to incorporate domain-specific knowledge, and they can fine-tune models to meet the ever-evolving needs of the business. Building an AI model also unleashes creativity, empowering builders to find the solution that’s just right for their specific needs. Whether you’re a coding expert or a total beginner, here are three ways to build your first AI model. You can choose the one that best fits your use cases, tech stack, existing systems, and data types.
No-code/low-code platforms (Easiest)
Data analysts and other business professionals can bypass major coding hurdles with no-code or low-code platforms. Pecan offers a free trial where analysts can build a model in minutes.
This approach is straightforward, akin to buying several cakes and frostings with different flavors to find the best taste, rather than baking from scratch. It empowers users to focus on the ultimate business value of predictive modeling without getting bogged down in the baking process details.
Building an AI model becomes as simple as dragging, dropping, and clicking. With comprehensive guidance throughout the process, anyone can design workflows, connect common business data sources, and configure model parameters.
For Pecan, users only need familiarity with SQL to leverage their data for predictive modeling.
While lacking the flexibility of low-code platforms, these solutions remain powerful, quickly understanding relevant data patterns, making predictions, and guiding decisions. They are ideal for real-time decision-making and rapid deployment without coding hassles.
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AutoML (The middle ground)
If no-code platforms are like buying a designer cake and programming is akin to baking from scratch, automated machine learning (AutoML) is comparable to using a box of premixed cake mix. Just add the wet ingredients, mix, and bake.
It’s a balanced approach, offering convenience and customisation. Platforms like Google AutoML, H2O.ai, and Azure AutoML automate the training process, including feature selection, hyperparameter tuning, and model evaluation. While these platforms streamline the process to some extent, users still benefit from domain knowledge and technical expertise to achieve optimal results.
Traditional programming and machine learning libraries (Hardest)
Are users proficient in Python and popular libraries like scikit-learn, TensorFlow, or PyTorch?
If so, they can utilise their coding skills to build their own AI model. This approach is akin to baking a cake from scratch (without a recipe!): consider ingredients, precise measurements, and baking times. It involves trial and error, experimenting until perfection is achieved.
As experienced data analysts or data scientists, users can flex their data preprocessing, algorithm selection, training, and evaluation skills. This method is ideal for creating models directly implementable within organisations but requires advanced technical prowess and programming language proficiency.
