Significance of MLOps
Before developing and maintaining machine learning models, it is important to follow a set of MLOps principles. Process completion involves three stages, application design, development & experiments, testing, and operations. Practicing the MLOps principles allow data scientists, machine learning engineers, and DevOps teams to produce efficient systems. Besides a set of principles, MLOps is becoming useful as an approach to managing the machine learning lifecycle in its entirety. Lifecycle management allows integration with software, deployment, and governance.
ML Application Design
In the beginning phase, the MLOps process involves identifying your users, designing the ML model for problem-solving, and assessing project development. The designing stage requires inspecting data for model training and specifying the required functions of ML infrastructure models. Those are the requirements to design the architecture, ML application, and create a testing suite for models in the future.
ML Development & Experiments
Designing the machine learning infrastructure is a follow-up stage referred to as the ML development and experiment phase. You can verify the ML model's relevancy by running various processing steps, including data engineering and identifying algorithms.
AI ML Infrastructure Testing
When training a model, developers use the same data that can produce other identical machine learning models. An essential requirement is testing for training the models, algorithmic accuracy, API usage, integration, and model validation.
We serve clients in 20 countries building DVC, and other developer tools for machine learning. Our well-funded team has expertise and skills to solve complex datasets management, machine learning infrastructure, and model lifecycle management. We created our first version of DVC in 2017 as an open source and one year later incorporated Iterative in 2018. In 2020, we released DVC 1.0 and Studio in 2021.
Our Products Offerings
- DVC Studio is a product used by machine learning researchers, managers, and practitioners to collaborate with team members and the public. It allows you to create visual reports for sharing within an organization.
- DVC is a tool that builds data models and tracks experiments.
- CML enables developers to automatically train models and generate real-time reports in the ml infrastructure.
- VS Code Extension is a product specifically for local ML model development and experimental tracking.
DVC Studio Features
Check out DVC Studio Features! Our DVC Studio product enables professionals to track machine learning experiments, collaborate, and creative visualization. With its capabilities to perform automated bookkeeping, you can easily streamline the sharing and collaboration of knowledge among professionals. DVC Studio features include:
- Unlimited number of Git repositories connection.
- Project sharing with teams and the public.
- Running experiments.
- Plots visualization.
- Data centric comparison of experiments.
- Integration with common cloud providers, including Azure, AWS, GCP, and Kubernetes.
Contact us today! Learn more about our product offerings.