Question: I need a platform that can help me build consumer ML pipelines without requiring extensive coding knowledge.

If you're looking for a platform to build consumer ML pipelines without needing to know how to code, Cortex is a no-code machine learning platform that automates the entire pipeline. It lets data scientists build new pipelines in less than 30 minutes, which is great for getting around data problems and for running machine learning decisions in real time. Its features include ingesting raw data, building consumer ML pipelines, automating dynamic decisions, and integrating with a variety of data sources like data lakes, warehouses and CRMs.

Another good option is PI.EXCHANGE, which also offers an end-to-end ML pipeline with no coding required. It includes data preparation with repeatable recipes and data visualization tools, model development with recommended features and algorithms, and integration with a variety of tools and platforms. With its collaborative environment and variety of machine learning problem types, PI.EXCHANGE is good for a variety of industries and use cases, so it's a good option for teams and individuals.

DataChat is another powerful platform for anyone, regardless of technical ability. It combines the look and feel of a spreadsheet and chat software so you can analyze data through chat boxes and spreadsheets. DataChat automates data preparation, exploration and modeling, which makes it good for data scientists, analysts and business users who need to make decisions fast and with confidence. It offers a 14-day free trial and pricing that scales with business needs.

Last, Obviously AI automates data science workflows, letting you build and deploy machine learning models without heavy coding. It's good for a variety of use cases like churn prediction and dynamic pricing, with features like fast model creation, one-click deployment and automated model monitoring. With integrations like Zapier and Salesforce, Obviously AI lets you build AI models into existing workflows, which is good for decision-making processes that span different jobs.