ayush714/mlops-projects-course

mlops-projects-course

Unlock High-Paying Job Offers with MLOps Project Mastery

Welcome to a transformative journey where we don't just learn Machine Learning Operations (MLOps) but master the art of securing high-paying job offers, both nationally and internationally, all while working remotely from the comfort of your home!

🚀 Course Overview

This isn’t just another machine learning course. This is a golden ticket to elevating your projects and take-home challenges with a creamy layer of MLOps, ensuring you stand out in every job application and project proposal. I've utilized these strategies to secure my first Data Center internship at a US-based startup, earning 2x more than what Google India pays its software engineers, and to receive numerous job offers from companies across the U.S., U.K., Germany, and more.

What Will You Learn?

• Fundamentals of MLOps: Dive deep into the core concepts and applications of MLOps.
• End-to-End Project: Engage in a comprehensive project, spanning from data ingestion to deployment, utilizing state-of-the-art tools like MLflow, ZenML, and more. ( Creamy Layer Strategy: Learn how to add that extra 'oomph' to your projects and challenges, making them irresistible to potential employers or clients.

🌟 About the Instructor

Hello! I'm Ayush Singh, the Data Scientist at Replayed, Founder - SecondBrainLabs, and your guide on this journey. I've led several products in the creators' economy and worked as an MLOps engineer on one of the fastest-growing MLOps frameworks, ZenML. With experience as a Data Scientist at Artifact and building large-scale NLP products even before GPT was launched, I bring to the table a wealth of knowledge and practical insights that will enrich your learning experience.

🛠 Course Structure

• Module 1: Introduction to MLOps
• Module 2: Deep Dive into MLOps Tools
• Module 3: Real-World Project from Ingestion to Deployment

🎯 Who Should Enroll?

• Aspiring Data Scientists: Who wish to elevate their projects and secure high-paying job offers.
• ML Practitioners: Looking to add a competitive edge to their skillset with MLOps.
• Anyone Curious: About leveraging MLOps for career growth.

📚 Resources & Links

All the required resources, additional reading materials, and tools links are provided in the course modules. Ensure to check them out to enhance your learning and practical application of the concepts.

mlops-projects-course's Issues

Error in Model Deployment

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Python == 3.11.8

mlflow == 2.10.2
mlserver == 1.5.0
mlserver-mlflow == 1.5.0
MarkupSafe == 2.1.5
numpy == 1.26.4
pandas == 2.2.1
scikit-learn == 1.4.1.post1
tqdm == 4.66.2
zenml == 0.55.5
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I have been following the code of the video lecture. The previous versions of the pipeline ran well. That was until trying to deploy the model.
I have made several virtual environments and used different stacks (deleted one stack and created another one and set that up (The latest stack used was: mlflow_customer_02.
I still cannot make the deployment work.

This is the main error:

ConnectionError: HTTPConnectionPool(host='127.0.0.1', port=8237): Max retries exceeded with 
url: /api/v1/runs/d1673d8a-89aa-42c0-a805-53d3fa8f99ac?hydrate=True (Caused by 
NewConnectionError('<urllib3.connection.HTTPConnection object at 0x28f196350>: Failed to 
establish a new connection: [Errno 61] Connection refused'))

Tried to do this as well and did not work:

% zenml down
% zenml disconnect
% zenml up
% python run_deployment.py --config deploy

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A summary of the steps retrieved to show that the pipeline works until the deployment phase:

% python run_deployment.py --config deploy

Initiating a new run for the pipeline: continuous_deployment_pipeline.
Reusing registered pipeline version: (version: 13).
Executing a new run.
Caching is disabled by default for continuous_deployment_pipeline.
Using user: default
Using stack: mlflow_stack_customer_02
  model_deployer: mlflow_customer_02
  experiment_tracker: mlflow_tracker_customer_02
  orchestrator: default
  artifact_store: default
Step ingest_df has started.
Ingesting data from /Users/luis/Documents/.../venv_0754_FCC_MLOPS_MLProd_Projects_311_01/data/olist_customers_dataset_copy01.csv
Step ingest_df has finished in 2.512s.
Step clean_df has started.
Data cleaning completed
Step clean_df has finished in 1.542s.
Step train_model has started.
Model training completed
Model Trained Successfully
Step train_model has finished in 3.099s.
Step evaluate_model has started.
Calculating MSE
MSE: 1.864077053397548
Calculating R2 Score
R2 Score: 0.017729030402295565
Calculating RMSE
RMSE: 1.3653120717980736
Step evaluate_model has finished in 0.683s.
Step deployment_trigger has started.
Step deployment_trigger has finished in 0.095s.
Caching disabled explicitly for mlflow_model_deployer_step.
Step mlflow_model_deployer_step has started.
Calling stop method...
stop method executed successfully.
Updating an existing MLflow deployment service: MLFlowDeploymentService[577b7471-9979-487c-94fb-cc6ede12b61d] (type: model-serving, flavor: mlflow)
Calling stop method...
stop method executed successfully.
Calling start method...
⠏ Starting service 'MLFlowDeploymentService[577b7471-9979-487c-94fb-cc6ede12b61d] (type: 
model-serving, flavor: mlflow)'.

File "/Users/luis/miniforge3/envs/venv_0754_FCC_MLOPS_MLProd_Projects_311_02/lib/python3.11/site-packages/zenml/services/service.py", line 461, in start
    raise RuntimeError(
RuntimeError: Failed to start service MLFlowDeploymentService[577b7471-9979-487c-94fb-cc6ede12b61d] (type: model-serving, flavor: mlflow)
  Administrative state: active
  Operational state: inactive
  Last status message: 'service daemon is not running'
For more information on the service status, please see the following log file: /Users/luis/Library/Application Support/zenml/local_stores/19914fc0-6d0d-41d4-bca6-4924211935c1/577b7471-9979-487c-94fb-cc6ede12b61d/service.log

Retrying (Retry(total=9, connect=5, read=None, redirect=None, status=None)) after connection broken by 'RemoteDisconnected('Remote end closed connection without response')': /api/v1/steps/feeec1ee-8f5e-41ae-87f2-d803fd045f31

(…)

Retrying (Retry(total=9, connect=5, read=None, redirect=None, status=None)) after connection broken by 'RemoteDisconnected('Remote end closed connection without response')': /api/v1/steps/feeec1ee-8f5e-41ae-87f2-d803fd045f31

ConnectionError: HTTPConnectionPool(host='127.0.0.1', port=8237): Max retries exceeded with 
url: /api/v1/runs/d1673d8a-89aa-42c0-a805-53d3fa8f99ac?hydrate=True (Caused by 
NewConnectionError('<urllib3.connection.HTTPConnection object at 0x28f196350>: Failed to 
establish a new connection: [Errno 61] Connection refused'))

—————
Below is more stack information
—————

% zenml stack describe

COMPONENT_TYPE	COMPONENT_NAME
MODEL_DEPLOYER	mlflow_customer_02
EXPERIMENT_TRACKER	mlflow_tracker_customer_02
ORCHESTRATOR	default
ARTIFACT_STORE	default

'mlflow_stack_customer_02' stack (ACTIVE)
Stack 'mlflow_stack_customer_02' with id 'c314644e-6abc-45a8-b8fa-271fff858b6c' is
owned by user default.
Dashboard URL:
http://127.0.0.1:8237/workspaces/default/stacks/c314644e-6abc-45a8-b8fa-271fff858b
6c/configuration

—————

% zenml status

-----ZenML Server Status-----
Connected to a ZenML server: 'http://127.0.0.1:8237'
The active user is: 'default'
The active workspace is: 'default' (repository)
The active stack is: 'mlflow_stack_customer_02' (repository)
Active repository root: /Users/luis/Documents/.../venv_0754_FCC_MLOPS_MLProd_Projects_311_02
Using configuration from: '/Users/luis/Library/Application Support/zenml'
Local store files are located at: '/Users/luis/Library/Application
Support/zenml/local_stores'
The status of the local dashboard:

| ZenML server 'local' | |
| URL | http://127.0.0.1:8237 |
| STATUS | ✅ |
| STATUS_MESSAGE | |
| CONNECTED | ✅ |

—————

% zenml stack list

ACTIVE	STACK NAME	STACK ID	OWNER	MODEL_DEPLOYER	EXPERIMENT_TRACKER	ORCHESTRATOR	ARTIFACT_STORE
👉	mlflow_stack_customer_02	c314644e-6abc-45a8-b8fa-271fff858b6c	default	mlflow_customer_02	mlflow_tracker_customer_02	default	default
	default	aeff7473-997f-47a9-87fd-9d771f7543b6	-			default	default
	mlflow_stack_customer	6a772157-30ec-463b-999f-10299ce3ec95	default	mlflow_customer	mlflow_tracker_customer	default	default

—————

% zenml logs

INFO:     127.0.0.1:50527 - "GET 
/api/v1/steps?hydrate=False&sort_by=created&logical_operator=and&page=1&size=20&scope_workspac
e=fd2a5d49-22cc-4dc8-a986-fa27bc93b88d&pipeline_run_id=d1673d8a-89aa-42c0-a805-53d3fa8f99ac 
HTTP/1.1" 200 OK

INFO:     127.0.0.1:50527 - "POST /api/v1/steps HTTP/1.1" 200 OK
objc[5368]: +[__NSCFConstantString initialize] may have been in progress in another thread 
when fork() was called.

objc[5368]: +[__NSCFConstantString initialize] may have been in progress in another thread 
when fork() was called. We cannot safely call it or ignore it in the fork() child process. 
Crashing instead. Set a breakpoint on objc_initializeAfterForkError to debug.

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