Hello Tino,good question! For any tech company focusing on making intelligent products (or data powered) setting up MLOps is recommended (to save time, money and energy). It saves a lot of time for data scientists and SE team from mundane tasks (deployments, manual tests, repetitive data engineering tasks, manual debugging etc) and enables them to focus on what truly matters (making best models and learning in realtime from data) by taking benefits of automation via CI-CD pipeline to avoid mundane jobs. Yes, before implementing in live/production it is recommended to test the pipeline and monitoring features (model, data, feature drifts etc) in DEV and QA environments to validate if the MLOps pipeline provides business value or not (measure with business KPI’s), only then go live/to production.

Okay cool 🙂 Got it 🙂 Thanks!!

Yes, there will be some differences b/w MLOps pipelines for smaller vs big organisations based on their business needs, teams and data processing abilities. Check out chapter 2: Characterizing Your Machine Learning Problem in the book. It explains on this on detail 🙂 (screenshot from chapter 2, figure 2.9)

I’ll expand this question a bit, - does best practices (or some tips&tricks, or even tech stack?) differ for small vs big teams?

Yes, they differ case by case (company by company) especially the tools. But some of the the principles remain same on high level (MLOps pipeline to build, deploy and monitor ML models). For small companies, most likely they need to build the MLOps platform with limited, budget both in money and time and their operations might be small on scale. Big companies have high volume of data, operations and teams so the setup of MLOps pipelines/platform will differ in most cases 🙂

Thanks for your input! Appreciate a lot! 🙌

Good question Diego! Knowing how to setup MLOps pipeline/platform (infra and architecture) is a bit too much for data scientists. However it is recommended for data scientist to know how to work with features of MLOPs pipelines/platform (once they are setup) such as registering datasets, models and packaging them on the MLOps platform. This way DS’s can take the benefits of MLOps and focus on what they are good at ‘data/Stats/algorithms’. I hope this answers your question 🙂

Hello Chetna Cloud certifications are worth it, they give a 360 degree view on what cloud has to offer so you may pick and choose best services to solve your business problems (optimisation). They surely make the resume standout for MLOps role, companies are looking for ML engineers who know data engineering and infrastructure setup well (certified is better) 🙂

thanks 🙂

Lalit Pagaria Choosing right tools for the business problem is most important (not the other way around). Any cloud which has capabilites to serve your needs will do (these days most of them are good enough). Down side of cloud though is vendor lock, to avoid that we can use cloud agnostic/open source MLOps tools e.g. MLFlow and Valohai which can work with most of the clouds. So choosing right cloud/tools depends on the business problem at hand 🙂

Good question Matthew Emerick! MLOps addresses that constantly changing environment by adapting to the changing data/environment, optimising performance for changes, auto scaling and being relevant for the changing environment 🙂

Hard to generalize at this point as different companies are at different stages in their ML adoption 🙂

Nice question Mansi Parikh! If the company/organisation is sure of having ML models in their workflow then the sooner the better it is to think of implementing MLOps. Otherwise, When data pipelines are set up and the organisation has the needed data setup in place. The sooner the better it is 🙂

thank you so much, Emmanuel! this is great. I appreciate the thoughtful response. 🙂

Rushanthi It’s good for an ML Engineer to have experience in MLOPs (especially data engineering and platform setup). Data Scientist is the user of MLOPs platform, so it helps if they have some exp using MLOPs platforms to build and deploy models. Data analysts can do without it. Good question on where are we headed with automation - Time will say but probably MLOPs will impact every Data science/Engineering job roles (let’s hope positively) e.g. more efficiency, less time and resources.

Thanks a lot for the enlightenment Emmanuel. Appreciate it a lot for clearing up my puzzle 🙌

When I was working on projects that needed something I would call MLOps: creating a docker application, deploying on a google k8s cluster, making a pipeline using gitlab ci, I realized that I do not understand how it is working under the hood, but just looking for “recipes”, keywords and using tutorials or to a lesser extent documentation (written in a form of a “recipe” as well). Like: put this into gitlab-ci.yaml file, click on this, that and that in google cloud. Sure, it took a long time to make all the parts work together.
However, it makes me think that:

• there is no special knowledge involved into MLOps vs. ,say, data science where one is expected to know math or statistics.
  
• Therefore, it makes be a bit “afraid” that MLOps engineer will be either replaced by some automatic deployment solutions or
  
• simpler, young people who tend to grasp many new tools that appear.
  Thank you!

+1

Good question Oleg Polivin! MLOps person brings added value to a Data science team mainly in terms of infra setup/maintenance, monitoring ML Models/systems and maintaining pipelines. Sure it can be done/learned by others and MLOps engineers can may as well be replace (e.g. with SRE engineers, DEVOps engineers etc). For now it looks like data scientists are more on the verge of replacement (with AutoML) 😃 it’s not as easy to replace MLOps engineer though but maybe with time and more automation tools we might get there where MLOps engineers can be easily replaced.

+1

Hi Lamjed Debbich Thank you! To begin with I suggest to get a good theoretical understanding of MLOps workflow, learn how to build ML microservices using docker and deploy them on various deployment targets (Engineering MLOps book will give a great headstart on this). After that decide on MLOPs tools (cloud or opensource, e.g. Azure, MLFLow etc) you would like implement and then find resources that teach you implementation. Learning by doing is the best way to learn MLOps 🙂

I see that it’s often synonymous to ML engineer, which I don’t agree with. What’s your opinion about it?

From my understanding, there are (at least) 2 types of MLE defined by most of the companies. Some MLEs focus on the ML models algorithms, other MLEs focus on designing “data intensive application” or wee can call it MLOps? Again, ML in production is not just the ML algorithms — Andrew Ng.

Depends. If a person is needed for team to setup and monitor infra and operations it’s a good idea, otherwise Devops (or SRE) engineers with some knowledge of ML can enable MLOps 🙂

Haven’t looked at the content, can’t say much but for the look of it, looks like it’s more focused on ML Engineering/DS problems (e.g optimisation, robustness, efficient modelling etc) and not much on MLOps 🙂

Made with ML looks good (recommended), not sure of Udemy course or Andrew NG’s courses (haven’t looked at the content) 🙂

Hey Tino, it’s hard to generalize for all cases but depends on the use case we work on. For some cases data is too sensitive and can’t be worked using external tools. For those cases it is better to build on your own but otherwise plug and play solutions like fiddler, mlflow, valohai etc are awesome, using them will save a lot of time and energy 🙂