[Docs] Fix a few typos (mlrun#3933)

docs/cheat-sheet.md

@@ -475,7 +475,7 @@ context.log_dataset(key="model", df=df, format="csv", index=False)
 ### Track returning values using `hints` and `returns`
 
 - Pass type hints into the inputs parameter of the run method. Inputs are automatically parsed to their hinted type. If type hints are 
-not in code, they can be passed in the inputs keys. Hints use the structure: `key : type_hint`
+not in code, they can be passed in the input keys. Hints use the structure: `key : type_hint`
 - Pass log hints: how to log the returning values from a handler. The log hints are passed via the returns parameter in the run method. 
 A log hint can be passed as a string or a dictionary.
 - Use the `returns` argument to specify how to log a function's returned values.

docs/deployment/batch_inference.ipynb

@@ -403,7 +403,7 @@
     "\n",
     "To schedule a run, you can set the schedule parameter of the run method. The scheduling is done by using a cron format.\n",
     "\n",
-    "You can also schedule runs from the dashboard. On the Projects > Jobs and Workflows page, you can create a new job using the New Job wizard. At the end of the wizard you can set the job scheduling. In the following example, the job is set to run every 30 minutes.\n"
+    "You can also schedule runs from the dashboard. On the Projects > Jobs and Workflows page, you can create a new job using the New Job wizard. At the end of the wizard, you can set the job scheduling. In the following example, the job is set to run every 30 minutes.\n"
    ]
   },
   {

docs/feature-store/training-serving.md

@@ -17,7 +17,7 @@ import mlrun.feature_store as fstore
 svc = fstore.get_online_feature_service(<feature vector name>)
 ```
 
-After creating the service you can use the feature vector's entity to get the latest feature vector for it.
+After creating the service, you can use the feature vector's entity to get the latest feature vector for it.
 Pass a list of `{<key name>: <key value>}` pairs to receive a batch of feature vectors.
 
 ```python

docs/projects/automate-project-git-source.ipynb

@@ -1595,7 +1595,7 @@
    "source": [
     "## Running a workflow using a Git source\n",
     "To run the workflow, use the {py:class}`~mlrun.projects.MlrunProject.run` method. With {py:class}`~mlrun.projects.MlrunProject.run` you can run a workflow \n",
-    "or schedule a workflow using kubeflow pipelines by specifing the workflow name or the workflow file path.\n",
+    "or schedule a workflow using kubeflow pipelines by specifying the workflow name or the workflow file path.\n",
     "\n",
     "To specify running remote, use `remote:local` or `remote:kfp`."
    ]

docs/projects/git-best-practices.ipynb

@@ -139,7 +139,7 @@
    "id": "b994758b-6cf5-4c91-aa00-e4f1641471a1",
    "metadata": {},
    "source": [
-    "1. Initialize your repo using the command line as per [this guide](https://dev.to/bowmanjd/create-and-initialize-a-new-github-repository-from-the-command-line-85e) or using your version control software of choice (e.g. GitHub, GitLab, etc).\n",
+    "1. Initialize your repo using the command line as per [this guide](https://dev.to/bowmanjd/create-and-initialize-a-new-github-repository-from-the-command-line-85e) or using your version control software of choice (e.g. GitHub, GitLab, etc.).\n",
     "\n",
     "```bash\n",
     "git init ...\n",

docs/serving/model-serving-get-started.ipynb

@@ -152,7 +152,7 @@
     "\n",
     "You can use the `flow` topology to specify tasks, which typically manipulates the data. The most common scenario is pre-processing of data prior to the model execution.\n",
     "\n",
-    "```{note} Once the topology is set, you cannot change an existing function toplogy.\n",
+    "```{note} Once the topology is set, you cannot change an existing function topology.\n",
     "```\n",
     "\n",
     "In this topology, you build and connect the graph (DAG) by adding steps using the `step.to()` method, or by using the \n",

docs/serving/serving-graph.md

@@ -20,7 +20,7 @@ The serving graphs can be composed of [pre-defined graph steps](./available-step
 data readers and writers, data engineering tasks, validators, etc.), [custom steps](./writing-custom-steps.html), or from native python 
 classes/functions. A graph can have data processing steps, model ensembles, model servers, post-processing, etc. (see the [Advanced Model Serving Graph Notebook Example](./graph-example.html)). Graphs can auto-scale and span multiple function containers (connected through streaming protocols).
 
-![serving graph high level](../_static/images/serving-graph-high-level.png)
+![serving graph high-level](../_static/images/serving-graph-high-level.png)
 
 Different steps can run on the same local function, or run on a remote function. You can call existing functions from the graph and reuse 
 them from other graphs, as well as scale up and down the different components individually.

docs/training/built-in-training-function.ipynb

@@ -351,7 +351,7 @@
     "\n",
     "**Logging parameters**\n",
     "\n",
-    "*Parameters to control the automatic logging feature of MLRun. You can adjust the logging outputs as relervant, and if\n",
+    "*Parameters to control the automatic logging feature of MLRun. You can adjust the logging outputs as relevant, and if\n",
     "not passed, a default list of artifacts and metrics is produced and calculated.*\n",
     "\n",
     "* `_artifacts`: `Dict[str, Union[list, dict]]` — Additional artifacts to produce post training. See the\n",

docs/tutorial/03-model-serving.ipynb

@@ -50,7 +50,7 @@
    "source": [
     "**Get or create a new project**\n",
     "\n",
-    "You should create, load or use (get) an [MLRun Project](https://docs.mlrun.org/en/latest/projects/project.html). The `get_or_create_project()` method tries to load the project from the MLRun DB. If the project does not exist it creates a new one."
+    "You should create, load or use (get) an [MLRun Project](https://docs.mlrun.org/en/latest/projects/project.html). The `get_or_create_project()` method tries to load the project from the MLRun DB. If the project does not exist, it creates a new one."
    ]
   },
   {

docs/tutorial/05-model-monitoring.ipynb

@@ -352,7 +352,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Once data drift has been calculated, you can view it in the MLRun UI. This includes a high level overview of the model status:"
+    "Once data drift has been calculated, you can view it in the MLRun UI. This includes a high-level overview of the model status:"
    ]
   },
   {

docs/tutorial/src/script.py

@@ -41,7 +41,7 @@ def clean_df(df):
 # To Compute Haversine distance
 def sphere_dist(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
     """
-    Return distance along great radius between pickup and dropoff coordinates.
+    Return distance along great radius between pickup and drop-off coordinates.
     """
     # Define earth radius (km)
     R_earth = 6371
@@ -63,7 +63,7 @@ def sphere_dist(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
 
 def sphere_dist_bear(pickup_lat, pickup_lon, dropoff_lat, dropoff_lon):
     """
-    Return distance along great radius between pickup and dropoff coordinates.
+    Return distance along great radius between pickup and drop-off coordinates.
     """
     # Convert degrees to radians
     pickup_lat, pickup_lon, dropoff_lat, dropoff_lon = map(
@@ -90,7 +90,7 @@ def radian_conv(degree):
 
 def add_airport_dist(dataset):
     """
-    Return minumum distance from pickup or dropoff coordinates to each airport.
+    Return minimum distance from pickup or drop-off coordinates to each airport.
     JFK: John F. Kennedy International Airport
     EWR: Newark Liberty International Airport
     LGA: LaGuardia Airport

mlrun/model.py

@@ -903,7 +903,7 @@ def notifications(self, notifications):
 
     def extract_type_hints_from_inputs(self):
         """
-        This method extracts the type hints from the inputs keys in the input dictionary.
+        This method extracts the type hints from the input keys in the input dictionary.
 
         As a result, after the method ran the inputs dictionary - a dictionary of parameter names as keys and paths as
         values, will be cleared from type hints and the extracted type hints will be saved in the spec's inputs type
@@ -986,7 +986,7 @@ def _separate_type_hint_from_input_key(input_key: str) -> Tuple[str, str]:
         # Validate correct pattern:
         if input_key.count(":") > 1:
             raise mlrun.errors.MLRunInvalidArgumentError(
-                f"Incorrect input pattern. Inputs keys can have only a single ':' in them to specify the desired type "
+                f"Incorrect input pattern. Input keys can have only a single ':' in them to specify the desired type "
                 f"the input will be parsed as. Given: {input_key}."
             )
 

mlrun/package/packagers/numpy_packagers.py

@@ -600,7 +600,7 @@ def unpack_file(
 
 class NumPyNumberPackager(DefaultPackager):
     """
-    ``numpy.number`` packager. It is also used for all `number` inheriting numpy objects (`float32`, uint8, etc).
+    ``numpy.number`` packager. It is also used for all `number` inheriting numpy objects (`float32`, uint8, etc.).
     """
 
     PACKABLE_OBJECT_TYPE = np.number

mlrun/runtimes/local.py

@@ -420,7 +420,7 @@ def exec_from_params(handler, runobj: RunObject, context: MLClientCtx, cwd=None)
     if runobj.spec.verbose:
         logger.set_logger_level("DEBUG")
 
-    # Prepare the inputs type hints (user may pass type hints as part of the inputs keys):
+    # Prepare the inputs type hints (user may pass type hints as part of the input keys):
     runobj.spec.extract_type_hints_from_inputs()
     # Read the keyword arguments to pass to the function (combining params and inputs from the run spec):
     kwargs = get_func_arg(handler, runobj, context)