AI Operationalization and MlOps tools
- Kubernetes
- Docker
- Consul
- Terraform
- TF Extended
- Mlflow
- Flyte
- Kustomize
- Kubeflow
- Etcd
- Vault
- Prometheus
- OpenTracing
- Hydra
- Linkerd
- Tekton
- Conda
- DVC Pipelines
- Bind9
- AWS EMR
- OpenShift
- Envoy
- Istio
- Delta lake
- Hudi
- Iceberg
- Metaflow
analysis
The common ground in ML Operationalization
- Continuous model retraining
Machine learning models need to be continuously enhanced, updated and adjusted for new business changes and new environments as the underlying data from clients and users keep constantly changing.
- Prioritizing Bandit tests
Multivariate optimizations and Multi-variable armed bandit tests are proven to be much more valuable for ML models that are used in competitive real-time use cases rather than the simple A/B testing.
- Optimizing for Serverless ML
Optimizing machine learning models to be cloud native and to use cloud multi-worker GPUs or TPUs from day one has proven to be the fastest and most guaranteed way to get our models to production in a very agile way.
Ending alerts and incidents fatigue with AIOps
IT operations is one of the newest frontiers AI is tackling today. The use of machine learning and predictive analytics (AIOps) to monitor and optimize IT daily operations opens the door to big cost savings.
Through the analysis of millions of infrastructure events and log files from servers, networks, databases and cloud platforms, AIOps software performs sophisticated root-cause analysis of incidents, anticipates future problems, automates fixes and assignment of new tickets which reduces incident noise and leads to a lower MTTR.
On the other hand, the use of predictive analytics drives IT service metrics and insights that allow operations managers and CTOs to uncover the areas where they can make savings and introduce further automation to improve the efficiency of IT services and offer a better user experience.
For a better future powered by Autonomy
As businesses are adopting more AI technology today to build smarter systems and machines, we invest in building products that are inherently or partially autonomous to ensure that we take part in the revolution that will most likely reshape the future of humanity.
