I started a bring your own cloud AutoML for smaller teams

As a data scientist, I watched too many of my best machine-learning models die in Jupyter notebooks. 😭 I would spend hours—sometimes weeks—training, testing, validating, analysing, and comparing models, only for the winner to go nowhere.

Then I discovered MLOps—and deployment became another maze 😩. FastAPI, MLflow, DVC, Docker, Kubernetes manifests, model registries, health monitoring, drift detection… one tool led to another, and the infrastructure began taking more time than the machine learning itself. 😮‍💨
That frustration is why I built #SceptreAI. Too many data scientists and small teams are trapped in the same cycle: notebooks, disconnected tools, patched-together frameworks, and endless hand-offs before a model can create real value.
#SceptreAI closes that gap with one Kubernetes-native tabular AutoML and MLOps workspace. Dataset versioning, profiling, resource-aware training, MLflow tracking, external validation, SHAP explainability, model promotion, drift analysis, and Kubernetes serving all live in one traceable workflow.
The goal is simple: less platform assembly, more scalable and observable machine learning, and a clearer answer to the questions that matter: Can we trust this model—and can we use it in prod?