All ML is AI, but not all AI is ML.
For future reference, this is an ever-growing collection of
resources and platforms for machine learning engineers, data analysts,
and DevOps
Discover and curate high-quality training data from trusted sources. This forms the foundation of any ML project, as the quality and quantity of your training data directly impacts model performance.
Select pre-trained models that align with your project requirements. Whether building from scratch or leveraging existing models, choosing the right architecture is crucial.
Optimise model performance by adjusting parameters for your specific use case. This step adapts pre-trained models to your unique requirements and data.
Deploy your models to scalable cloud infrastructure for reliable access and collaboration. This ensures your models are available when and where they're needed.
Demonstrate your model's capabilities through interactive demos and documentation. This helps stakeholders understand and interact with your models.
Integrate models into production environments with robust CI/CD pipelines. This step brings your models from development to real-world applications.
Track model performance and maintain data quality in production. Continuous monitoring ensures your models remain accurate and reliable over time.
While subjective, the categories have been curated based on my experience and research of the ML/AI landscape. They might change in the future.
I'm open to suggestions for new categories or subcategories.
Most of the products have been encountered while working on ML projects for startups. Feel free to suggest new products, services or tools which fit in the categories.
Coming soon.