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in the [Releases section]( of
the repository.

1. Once you are happy with the draft, click `Publish`.

Release Branches

For official releases, MLServer uses long-lived release branches.
These branches will always follow the `release/<major>.<minor>.x` pattern (e.g.
`release/1.2.x`) and will be used for every `<major>.<minor>.x` [official
release](versioning-scheme) (e.g. the `release/1.2.x` will be used for
`1.2.0`, `1.2.1`, etc.).
Note that these branches will always be pushed straight to the main
`` and not to a fork and will never get merged with

Therefore, when starting a new **major or minor official release** please
create a `release/<major>.<minor>.x` branch.
Alternatively, when preparing a **patch official release**, please
_cherry-pick_ all relevant merged PRs from `master` into the existing
`release/<major>.<minor>.x` branch.

Versioning Scheme

The MLServer project publishes three types of release versions:

- **Dev pre-releases**, used to test new features before an official release.
They will follow the schema `<next-minor-version>.dev<incremental-index>`
(e.g. `1.2.0.dev3`).
- **Release candidates**, used to test an official release before the actual
release occurs.
This type of releases can be useful to test minor releases across different
projects. They follow the schema `<next-minor-version>.rc<incremental-index>`
(e.g. `1.2.0.rc1`).
- **Official releases**, used only for actual public releases. The version tag
will only contain the next minor version (e.g. `1.2.0`), without any

Based on the above, a usual release cycle between two official releases would
generally look like the following (where stability increases as you go down on
the chart):

![Versioning Scheme](./docs/assets/versioning-scheme.png)

Release Artefacts

Each release of MLServer will build and publish a set of artifacts, both at the
runtime level and the wider MLServer level:

- Docker image containing every inference runtime maintained within the
MLServer repo, tagged as `seldonio/mlserver:<version>` (e.g.
Note that this image can grow quite large.
- _Slim_ Docker image containing only the core MLServer package (i.e. without
any runtimes), tagged as `seldonio/mlserver:<version>-slim` (e.g.
This image is used, as the default for custom runtimes.
- Python package for the core MLServer modules (i.e. without any runtime),
which will get published in PyPI, named simply `mlserver`.
- For each inference runtime (e.g. `mlserver-sklearn`, `mlserver-xgboost`,
- Docker image containing only that specific runtime, tagged as
`seldonio/mlserver:<version>-<runtime>` (e.g.
- Python package for the specific runtime, which will get published in PyPI
(e.g. `mlserver-sklearn==1.2.0`).


In line with MLServer’s close relationship with the MLflow team, this release of MLServer introduces support for the recently released MLflow 2.0. This introduces changes to the drop-in MLflow “scoring protocol” support, in the MLflow runtime for MLServer, to ensure it’s aligned with MLflow 2.0.

MLServer is also shipped as a dependency of MLflow, therefore you can try it out today by installing MLflow as:

$ pip install mlflow[extras]

To learn more about how to use MLServer directly from the MLflow CLI, check out the [MLflow docs](

New Contributors
* [johnpaulett]( made their first contribution in
* [saeid93]( made their first contribution in
* [RafalSkolasinski]( made their first contribution in
* [dumaas]( made their first contribution in
* [Salehbigdeli]( made their first contribution in
* [regen100]( made their first contribution in

**Full Changelog**:


<a name="1.2.0.dev1"></a>
[v1.2.0.dev1]( - 01 Aug 2022


<a name="1.1.0"></a>
[v1.1.0]( - 01 Aug 2022






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