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Free Project Planning: We dive into your goals and current state to prepare before a kickoff.
Pay-as-you-go: Use our capacity when you need it, none of that retainer nonsense.
Experts On-Demand: Get new experts from our team when you need specific knowledge or consultation.
We Don't Sleep: Just kidding we do sleep, but we can flexibly hop on calls when you need.
Ad-hoc Calls: When a video call works better than a chat, we hop on a call together.
How it works?
Testimonials
We got to meet Michael from MeteorOps through one of our employees. We needed DevOps help and guidance and Michael and the team provided all of it from the very beginning. They did everything from dev support to infrastructure design and configuration to helping during Production incidents like any one of our own employees. They actually became an integral part of our organization which says a lot about their personal attitude and dedication.
I was impressed at how quickly they were able to handle new tasks at a high quality and value.
Good consultants execute on task and deliver as planned. Better consultants overdeliver on their tasks. Great consultants become full technology partners and provide expertise beyond their scope.
I am happy to call MeteorOps my technology partners as they overdelivered, provide high-level expertise and I recommend their services as a very happy customer.
I was impressed with the amount of professionalism, communication, and speed of delivery.
From my experience, working with MeteorOps brings high value to any company at almost any stage. They are uncompromising professionals, who achieve their goal no matter what.
Working with MeteorOps was exactly the solution we looked for. We met a professional, involved, problem solving DevOps team, that gave us an impact in a short term period.
They are very knowledgeable in their area of expertise.
You guys are really a bunch of talented geniuses and it's a pleasure and a privilege to work with you.
Thanks to MeteorOps, infrastructure changes have been completed without any errors. They provide excellent ideas, manage tasks efficiently, and deliver on time. They communicate through virtual meetings, email, and a messaging app. Overall, their experience in Kubernetes and AWS is impressive.
Nguyen is a champ. He's fast and has great communication. Well done!
They have been great at adjusting and improving as we have worked together.
Kubeflow is an open-source project developed by Google and aimed at making deployments of machine learning (ML) workflows on Kubernetes simple, portable, and scalable. Kubernetes, often referred to as K8s, is an open-source system designed to automate deploying, scaling, and managing containerized applications.
Kubeflow provides a collection of cloud-native tools for different stages of a ML workflow. The goal is to build a comprehensive, yet flexible, platform for machine learning that can leverage Kubernetes' ability to manage distributed systems.
It consists of various components for model training, serving, and management, including:
- Jupyter notebooks for interactive data science work
- TensorFlow and PyTorch for model training and serving
- Katib for hyperparameter tuning
- Kubeflow Pipelines for end-to-end orchestration of ML workflows
- Metadata for tracking and managing metadata of ML workflows
- KFServing for serving models using a serverless framework
This way, data scientists can define their ML pipelines similarly to how they define regular Kubernetes applications, taking advantage of the scalability and reliability of Kubernetes and its strong ecosystem of tools.
MLOps, or Machine Learning Operations, is a multidisciplinary approach that bridges the gap between data science and operations. It standardizes and streamlines the lifecycle of machine learning model development, from data preparation and model training, to deployment and monitoring, ensuring the models are robust, reliable, and consistently updated. This practice not only reduces the time to production, but also mitigates the 'last mile' problem in AI implementation, enabling successful operationalization and delivery of ML models at scale. MLOps is an evolving field, developing in response to the increasing complexity of ML workloads and the need for effective collaboration, governance, and regulatory compliance.
- MLOps allows for streamlined model deployment by standardizing the pipeline from development to production.
- The use of MLOps encourages effective communication between data scientists, engineers, and other stakeholders which enhances decision-making processes and results in robust machine learning applications.
- With the incorporation of concepts like continuous integration, delivery, and training, MLOps ensures that models are always updated, thoroughly tested, and smoothly deployed.
- Automated quality assurance and validation of machine learning models are inherent features of MLOps, which improve the reliability and performance of the models in production.
- MLOps frameworks are equipped with capabilities for ongoing monitoring of model performance and system health, facilitating early detection and resolution of any potential issues.
- MLOps ensures that all models conform to necessary regulatory and governance requirements, a critical consideration in highly-regulated sectors like finance and healthcare.
- By creating an efficient system for model operationalization and delivery, MLOps effectively addresses the 'last mile' problem of machine learning implementation.
- Model reproducibility is promoted by MLOps and it also offers a version control system for ML models which is vital for debugging and model improvements.
- MLOps aids in efficient management of computational resources which in turn helps in reducing operational costs.
- By providing a controlled environment for ML model deployment, MLOps mitigates risks associated with the introduction of new models or updates in the production environment.
Here are some reasons to use and benefits of Kubeflow:
- Kubeflow simplifies the deployment of machine learning workflows, making the process of managing and scaling these workflows easier.
- It allows the execution of ML workflows in a consistent manner across multiple platforms due to its ability to run on any Kubernetes-enabled platform, promoting portability and interoperability.
- It can streamline and automate the ML pipeline, enabling seamless integration, testing, delivery, and deployment of ML models.
- The use of Kubeflow can lead to improved collaboration among data scientists, DevOps teams, and other stakeholders, thanks to its open and flexible framework.
- By offering scalable serving of machine learning models, Kubeflow enables ML teams to meet changing demand dynamically.
- Kubeflow comes with built-in support for many popular machine learning libraries like TensorFlow and PyTorch, making the process of model training and serving more straightforward.
- It provides an organized way to keep track of experiments, including parameters, results, and associated artifacts, thereby enhancing reproducibility and accountability in ML workflows.
- Kubeflow aids in optimizing resource usage by allowing distributed model training over a Kubernetes cluster.
- By providing a controlled environment for deploying ML workflows, Kubeflow helps in mitigating risks associated with launching new models or updates in the production environment.
