Why Your Business Needs MLOps

Most ML systems don’t fail because of poor models. They fail at the systems level! You can have a world-class model architecture, but if you can’t reproduce your training runs, automate deployments, or monitor model drift, you don’t have a reliable system. You have a science project. That’s where MLOps comes in. 🔹 𝗠𝗟𝗢𝗽𝘀 𝗟𝗲𝘃𝗲𝗹 𝟬 - 𝗠𝗮𝗻𝘂𝗮𝗹 & 𝗙𝗿𝗮𝗴𝗶𝗹𝗲 This is where many teams operate today. → Training runs are triggered manually (notebooks, scripts) → No CI/CD, no tracking of datasets or parameters → Model artifacts are not versioned → Deployments are inconsistent, sometimes even manual copy-paste to production There’s no real observability, no rollback strategy, no trust in reproducibility. To move forward: → Start versioning datasets, models, and training scripts → Introduce structured experiment tracking (e.g. MLflow, Weights & Biases) → Add automated tests for data schema and training logic This is the foundation. Without it, everything downstream is unstable. 🔹 𝗠𝗟𝗢𝗽𝘀 𝗟𝗲𝘃𝗲𝗹 𝟭 - 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 & 𝗥𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 Here, you start treating ML like software engineering. → Training pipelines are orchestrated (Kubeflow, Vertex Pipelines, Airflow) → Every commit triggers CI: code linting, schema checks, smoke training runs → Artifacts are logged and versioned, models are registered before deployment → Deployments are reproducible and traceable This isn’t about chasing tools, it’s about building trust in your system. You know exactly which dataset and code version produced a given model. You can roll back. You can iterate safely. To get here: → Automate your training pipeline → Use registries to track models and metadata → Add monitoring for drift, latency, and performance degradation in production My 2 cents 🫰 → Most ML projects don’t die because the model didn’t work. → They die because no one could explain what changed between the last good version and the one that broke. → MLOps isn’t overhead. It’s the only path to stable, scalable ML systems. → Start small, build systematically, treat your pipeline as a product. If you’re building for reliability, not just performance, you’re already ahead. Workflow inspired by: Google Cloud ---- If you found this post insightful, share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more deep dive AI/ML insights!

 Let's talk about why MLOps engineers are becoming super important while traditional ML roles are kind of fading out. It's not because of a decline in technology; it's actually because of large language models (LLMs). One data scientist can now use GPT-4 and Claude to run tons of experiments in a day—more than an entire team could do in a whole sprint before. Fine-tuning is mostly about using ready-made scripts and solutions now. Product teams are putting their models straight into API setups, skipping a ton of steps. But then things start to go wrong. Maybe the costs spiral out of control unexpectedly. When something breaks in production, who fixes it? LLMs do a good job at writing code, but they can't build robust pipelines that handle data drift. They won't realize when a model's performance dips suddenly without any new deployments. They can't handle training when GPU spot instances disappear mid-process. They won't deal with broken schemas in a feature store or pinpoint where data contracts went wrong. They won't sync the deployment logic with monitoring, rollback strategy, product behavior, and real user traffic. And they certainly don’t go to meetings to clarify issues in plain language, without shifting the blame to "it works on my machine." So, this is why MLOps and DevOps folks are more essential than ever. Sure, writing code has gotten easier with the abundance of pretrained models. But making sure that's stable, observable, and actually works when shipped? That’s tough. And someone needs to take charge of that. I think we all know who that someone is now. #mlops #ml #ai #devops #futureofwork

There are 3 ingredients that pretty much guarantee the failure of any Machine Learning project: having the Data Scientists training models in notebooks, having the data teams siloed, and having no DevOps for the ML applications! Interestingly enough, that is where most companies trying out ML get stuck. The level of investment in ML infrastructures for companies is directly proportional to the level of impact they expect ML to have on the business. And the level of impact is, in turn, proportional to the level of investment. It is a vicious circle! Both Microsoft and Google established standards for MLOps maturity that capture the degree of automation of ML practices, and there is a lot to learn from those:  - Microsoft: https://lnkd.in/gtzDcNb9   - Google: https://lnkd.in/gA4bR77x Level 0 is the stage without any automation. Typically, the Data Scientists (or ML engineers, depending on the company) are completely disconnected from the other data teams. That is the guaranteed failure stage! It is possible for companies to pass through that stage to explore some ML opportunities, but if they stay stuck at that stage, ML is never going to contribute to the company's revenue. The level 1 is when there is a sense that ML applications are software applications. As a consequence, basic DevOps principles are applied at the software level in production, but there is a failure to realize the specificity of ML operations. In development, data pipelines are better established to streamline manual model development. At level 2, things get interesting! ML becomes significant enough for the business that we invest in reducing model development time and errors. Data teams work closer as model development is automated and experiments are tracked and reproducible. If ML becomes a large driver of revenue, level 3 is the minimum bar to strive for! That is where moving from development to deployment is a breeze. DevOps principles extend to ML pipelines, including testing the models and the data. Models are A/B tested in production, and monitoring is maturing. This allows for fast model iteration and scaling for the ML engineering team. Level 4 is FAANG maturity level! A level that most companies shouldn't compare themselves to. Because of ads, Google owes ~70% of its revenue to ML and ~95% for Meta, so a high level of maturity is required. Teams work together, recurring training happens at least daily, and everything is fully monitored. For any company to succeed in ML, teams should work closely together and aim for a high level of automation, removing the human element as a source of error. #MachineLearning #DataScience #ArtificialIntelligence -- 👉 Register for the ML Fundamentals Bootcamp: https://lnkd.in/gasbhQSk --