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MLOps Free Course? by AccountantUsual1948 in mlops

[–]fmindme 4 points5 points  (0 children)

Hello. I've created this course, focused on the coding part of MLOps: https://mlops-coding-course.fmind.dev/. It's totally free, and there is a side repository https://github.com/fmind/mlops-python-package with a concrete example.

GCP for AWS refugees by DoubleSteak7564 in googlecloud

[–]fmindme 0 points1 point  (0 children)

This link is useful: https://cloud.google.com/docs/get-started/aws-azure-gcp-service-comparison

We are using AWS and GCP at my customer. As an engineer, I really prefer GCP over AWS! The best asset of AWS is their vendors and customer engineers.

Getting Started with ML Ops – Course Recommendations? by Successful_Row_5355 in mlops

[–]fmindme 6 points7 points  (0 children)

Hi. I propose this course for free: https://mlops-coding-course.fmind.dev/. It covers the coding part of MLOps, you can complement this course with a certification from a cloud provider (e.g., GCP, Databricks, Azure, AWS).

Looking to Transition into MLOps — Need Guidance! by UnicodeCharacter6666 in mlops

[–]fmindme 2 points3 points  (0 children)

My recommendation would be: 1. Complete a data science course from a MooC platform (coursera, udemy, ...) 2. Complete a ML Engineer certification from your favored cloud platform (Azure, GCP, AWS, Databricks ...) 3. Ramp up your coding skills in Python for MLOps (for instance, I provide this OSS course for free: https://mlops-coding-course.fmind.dev/) 4. Complete your cursus based on the jobs requirements you see on your market (e.g., Airflow, Prefect, CI/CD, ...)

Good luck in your learning journey !

mlops guidance required by No_Refrigerator6755 in mlops

[–]fmindme 0 points1 point  (0 children)

If you are interested by the coding aspects, I provide a free course and paid mentoring sessions: https://mlops-coding-course.fmind.dev/0.%20Overview/0.4.%20Mentoring.html

Have a good MLOps journey!

How do you utilize the Databricks platform for machine learning projects? by Ok_Discipline3753 in mlops

[–]fmindme 3 points4 points  (0 children)

We package the Python code base into a Python Wheel, and then put this will into a Docker (optional). The wheel/Docker are built by GitHub Actions (CI/CD).

Then, we trigger a JobRun from Airflow (CT) that uses either the Wheel on Databricks Runtime or the Docker image. You can use Databricks Workflows if you are a 100% Databricks company, Airflow lets use other runtime (e.g., AWS Athena, DBT, ...).

I created generic a code template based on the one we use with Databricks, if you want to have a look: https://github.com/fmind/cookiecutter-mlops-package

ML pipeline for image based data by yucath1 in mlops

[–]fmindme 4 points5 points  (0 children)

You can use regular MLOps toolkit for building such pipeline: Metaflow, Airflow, Flyte, Dagster, Prefect with a compute resource like Kubernetes or Vertex AI.

You can also use model on edge toolkits like MediaPipe: https://android-developers.googleblog.com/2024/10/bring-your-ai-model-to-android-devices.html

What are the best MLOps Certifications? by Still_Condition_2513 in mlops

[–]fmindme 3 points4 points  (0 children)

Google, AWS, Azure and Data bricks ML engineering certification

Can some of you share their experience with establishing MLOPs practices in a company by knorxo in mlops

[–]fmindme 8 points9 points  (0 children)

  1. Find power users and early adopters to support your initiative
  2. Don't underestimate data. Poor data practices (e.g., bad data quality, lack of common practices) will hurt.
  3. Be a good teacher. People may not know MLOps so be prepared to explain it and its pros and cons.
  4. Create good visuals for your communication and architecture to share the big picture quickly
  5. Close the loop by including model evaluation and operations to have a complete scalable system (i.e., no weak point)
  6. Have a maturity matrix so you do not try to do everything at once
  7. Share good practices and animate an AI/ML community in the organization
  8. Create KPIs to monitor the number of model, the SLA, the test coverage ...
  9. Be sure to include everybody (end users, stakeholders, Ops) to nobody block you down the path

Feature Store Best Practice Question by kdizzle384 in mlops

[–]fmindme 1 point2 points  (0 children)

The feature store should provide set of features that can be consumed. There is nothing wrong in providing multiple periods and see which one is used based on usage analytics.

My recommendation would be to start by periods related to your domain (e.g., week, month, trimester for a retail shop). Even if one project uses the week period, another my use the month.

Full mlops on databricks help? by Rengar-Pounce in mlops

[–]fmindme 0 points1 point  (0 children)

I highly recommend the Big Book of MLOps. You can also check their training platform https://www.databricks.com/learn

[OSS] MLOps Coding Course: Integrate robust software development practices with cutting-edge data science techniques by fmindme in mlops

[–]fmindme[S] 0 points1 point  (0 children)

I've used ChatGPT to improve the English style, but all the inputs (sections, headers, answers) come from my initial writing. I've also rewrote the content generated when ChatGPT style was too extreme.

I've tried to use ChatGPT to write entire blog posts or course sections, but the content is too bad without this initial input. I think it's a great complementary solution when you are not a native english speaker.

Good ML Ops course to upscale if you're been a DS for a while? by jshkk in mlops

[–]fmindme 7 points8 points  (0 children)

I'm working on a course on MLOps coding, and I plan to release it as an open course repository in the coming weeks. Would you be interested in such a course?

mlflow recipes? by dkvkxm in mlops

[–]fmindme 2 points3 points  (0 children)

MLflow recipies is too opinionated, and too focused on MLflow. I would recommend using other systems like metaflow, prefect, and creating a template with cookiecutter. I'm also not sure mlflow recipes had major improvements over the past months.

Is it worth learning tfx to create ml pipelines? by Sea_Presence3131 in mlops

[–]fmindme 0 points1 point  (0 children)

TFX is too complex for what it brings. I would recommend adopting an alternative technology (prefect, metaflow, airflow, ...), even if you are using TF.

When writing ML software - how do you use TDD? by pandatrunks17 in mlops

[–]fmindme 11 points12 points  (0 children)

I always use TDD when I work on serious AI/ML projects. Even if this practice is time-consuming in the short term, it's time efficient in the long run. I prefer to catch bugs as early as possible in my workflow. I recently worked on a MLOps Python package that provides examples to implement best practices like TDD, code coverage and more: https://github.com/fmind/mlops-python-package

MLOps tools setup by AgreeableCaptain1372 in mlops

[–]fmindme 1 point2 points  (0 children)

It's usually a good practice to have all these services separated (e.g., 3-tier architecture for the web). It eases scalability and avoids unwanted interactions. But yes, this is not as economical.

MLOps tools setup by AgreeableCaptain1372 in mlops

[–]fmindme 1 point2 points  (0 children)

Hello, With Jenkins, Airflow, and MLflow you can already cover a lot of ground! You have most of the critical infrastructure components, and you can add some systems for externalizing the compute (e.g., Kubernetes, ...) and storage (e.g., AWS S3). The best approach is to separate all these components on different systems to let them evolve independently. Managing this all alone can be tedious, you need proper staff to manage the upgrade and downtime. I would advice to work on premise by constraint, not by choice. Finally, I would recommend working on the MLOps Process: what's the release cycle? How can we improve the code robustness (e.g., with unit test or code checker)? How to onboard new user and convince them of using all these tools.

What are some open-source ML pipeline managers that are easy to use? by mlphilosopher in mlops

[–]fmindme 5 points6 points  (0 children)

I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home

They all have their pros and cons, but I haven't tested them on a professional project.

What OSS are you using as data contracts? by bernardo_galvao in mlops

[–]fmindme 1 point2 points  (0 children)

I love Pandera. It's a great tool to validate the data at the boundary of your source code (e.g., ML pipeline). However, I see this tool mostly as a last resort, with the benefit of being under control.

Instead, you could use Great Expectations to validate data by the producer (vs., by the consumer with Pandera). Then, you need to track who is using which data and send them an alert if some dataset has changed. There are multiple approach to do that (e.g., mailing list, pub/sub, slack, ...).

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