Automat-it, the AWS Premier Partner and Managed Services Provider helping over 1,000 startups grow with AI, DevOps, and data, today announced it has achieved the Amazon Web Services (AWS) Machine Learning Competency.
The designation recognizes Automat-it for proven expertise in building, training, and running production machine learning on AWS, validated by AWS against technical best practices and real customer outcomes.
AWS awards the Machine Learning Competency only to partners it has vetted for technical skill and repeated customer success across the machine learning lifecycle. For a startup choosing who to trust with its machine learning workloads, the badge removes the guesswork. AWS itself has checked the work.
What the AWS Machine Learning Competency proves about Automat-it
The competency confirms Automat-it can take a startup from a model in a notebook to a monitored, retraining pipeline in production. Automat-it earned it on MLOps work delivered on AWS, built on Amazon SageMaker Pipelines, SageMaker Model Registry, MLflow, and SageMaker Model Monitor for drift detection.
Automat-it is DevOps-born, so its machine learning practice treats models the way it treats any production system: version-controlled, observable, cost-managed, and secured with role-based access from day one. The company holds 7 other AWS Competencies and the AWS Agentic AI Specialization, one of roughly 60 globally, placing its machine learning work inside a wider track record across data, GenAI, and cloud operations.
Why the competency matters for the startups Automat-it serves
Startups often find the infrastructure around the model is more difficult to maintain than the model itself. Training is ad-hoc, inference sits on always-on compute, and each new model means rebuilding plumbing from scratch. The AWS Machine Learning Competency shows that Automat-it has solved that problem repeatedly.
Feeld, the dating app, is a direct example. Its fraud-detection models ran on a scheduled notebook workflow that could not scale. Automat-it rebuilt the setup as a Terraform-managed, VPC-isolated AWS environment with serverless inference on AWS Lambda and daily automated retraining, giving Feeld stronger fraud detection, faster model iteration, and far less maintenance overhead.
Nick Gavriil, Lead Data Scientist at Feeld, said of the machine learning project with Automat-it:
“Automat-it has helped us transform our ML workflows into scalable production systems on AWS. Their technical expertise and hands-on collaboration allowed us to build a robust MLOps platform while keeping our focus on delivering real product value.”
How Automat-it delivers machine learning on AWS
Automat-it packages this work as a repeatable MLOps Accelerator rather than a one-off engagement. The accelerator sets up an automated ETL pipeline, a training pipeline with models saved to the Model Registry, MLflow experiment tracking, one-click deployment to production after testing and approval, and optional retraining triggered by data drift.
The result is production in weeks instead of months, on an AWS-validated architecture tuned by machine learning engineers. Startups still deciding where to start can begin with an assessment and get a prioritized architecture blueprint before committing to a build.
Automat-it’s Data Science team also provides valuable insights and real-world recommendations in machine learning projects. Specifically, they are responsible for selecting the best model for each application used.
A message from Automat-it
“Achieving the AWS Machine Learning Competency confirms what our customers already know,” explains Nir Shney-Dor, the VP of Global Solutions Architecture.
“We build machine learning the way we build everything on AWS, as production systems that are automated, observable, and cost-aware from day one, so startups can get models into production and prove value fast.”
Build machine learning with a proven partner
If your models are stuck in notebooks or your inference bill scales the wrong way, Automat-it can audit your current machine learning setup, identify the gaps, and hand back a concrete architecture blueprint and roadmap. Book a discovery call with the Automat-it team.
