Most workflow orchestrators were designed for data movement and then asked to handle machine learning, which is why so many ML pipelines are Airflow DAGs full of bash operators shelling out to training scripts. The mismatch is structural. A data pipeline is a sequence of steps that mostly need the same environment and the same modest resources. An ML pipeline has a data preparation step that needs lots of memory, a training step that needs four GPUs for six hours, and an evaluation step that needs neither, with typed artefacts flowing between them that are much larger and more structured than a row count.
Flyte was built from that observation and it still shows. The two design decisions that matter are per-task containerisation and strong typing. Each task declares its own container image and resource requirements, so the GPU step gets GPUs and the preprocessing step does not, and the orchestrator schedules accordingly on Kubernetes. Tasks declare typed inputs and outputs, so a workflow that passes a DataFrame where a model artefact is expected fails at registration rather than forty minutes into a training run.
Anyone who has lost an afternoon to a pipeline that ran for an hour before dying on a type mismatch will understand why this is not a small thing. Flyte 2 is a significant repositioning, described as a durable AI runtime rather than an ML orchestrator. The headline changes are writing workflows in plain Python without a constrained DSL, automatic recovery from infrastructure failures including out-of-memory kills, preempted nodes and GPU interruptions, and support for dynamic agent-native workflows that adapt at runtime rather than being fully specified up front. That last capability is the interesting one and the least proven.
Agentic pipelines, where the sequence of steps depends on model output rather than being known in advance, genuinely break the DAG abstraction, and the orchestration community has not settled on an answer. Flyte 2's approach is credible. The documentation supporting it is newer than the rest, and the advanced project guide, which builds an LLM reporting agent, is closer to a worked example than to a teaching sequence. The relationship between Flyte and Union.ai is handled more honestly than most open core arrangements.
Flyte is the open source project, Union.ai is a commercial superset using the same SDK and CLI, and the docs say so directly rather than obscuring the boundary. What they do less well is flag, while you are learning, which capability you are reading about belongs to which. Someone working through the docs can build a mental model of the platform and then discover that a piece of it requires the commercial product. That is a documentation problem rather than a licensing one and it is the sort of thing that erodes trust when you find out late.
The prerequisite that nobody should understate is Kubernetes. Flyte runs on Kubernetes and the abstraction does not hide it when something goes wrong. Debugging a failed task frequently means reading pod events, understanding resource requests and limits, and knowing why a scheduler could not place something. A team without Kubernetes competence will find this considerably harder than the quickstart suggests, and the docs, like all docs, are written from the position of everything working.
There is no course and no certification. The documentation is the entire structured learning offer, and while it is well organised, the migration guides in particular being a sign of a team that thinks about real adoption rather than greenfield demos, there is no syllabus and no credential. For a project this technically demanding, a proper course would be worth more than it would be for simpler tools. The community size question is real and worth weighing.
Airflow's ecosystem is vastly larger, which means the answer to your specific problem probably exists on a forum somewhere. Flyte's is smaller and more concentrated among ML platform teams, which means higher signal and fewer answers. Three point eight. A technically excellent orchestrator for the specific shape of ML and AI workloads, with documentation that is competent but is doing a job that deserves a proper course, and a Kubernetes dependency that sets the real difficulty level.