How do we achieve efficient machine learning operations?
The MLOps process starts with data. Data scientists spend most of their time exploring, preparing, and ingesting data. The work continues with identifying features for machine learning models, versioning the data, and splitting it into training, validation, and test datasets. To increase the productivity of machine learning engineers, our blueprints focus on high accessibility of quality data for use with our powerful analytical data platform and ML platform.
Most of MLOps capabilities are focused on model lifecycle management. Data scientists are usually familiar with the first stages of the lifecycle, but face challenges during production deployment of models. The final stages of the lifecycle include packaging of the production model, versioning it, and saving it in a repository. From there, the production model is used to generate insights. The MLOps toolbox should support a variety of machine learning algorithms - from advanced analytics to neural networks and deep learning.
The last mile in MLOps involves model serving - a machine learning model is deployed to production as part of an application or microservice. The deployment options can include cloud, datacenter, or edge. The insight delivery can be done either via Model-as-a-Service or by embedding a model into the consumer application. The model lifecycle doesn’t end there though. The model performance is monitored and the model automatically retrained if needed, ultimately achieving autonomous model operations.
Machine learning operations industries
We develop advanced artificial intelligence use cases and implement automated machine learning operations processes for Fortune-1000 enterprises in various industries including telecom, retail, media, gaming, and financial services.
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We use MLOps in all our AI projects. We can help you implement machine learning operations in your organization and power it with the modern ML platform. To get started, choose from the following engagement options and contact us to discuss the first steps.
We offer free half-day workshops with our top experts in machine learning operations to demonstrate the benefits of MLOps, discuss your machine learning strategy, challenges, optimization opportunities, and industry best practices.
If you have already decided to improve your machine learning process, but can’t commit to a large investment, we will help you identify an AI use case, onboard MLOps, and deliver tangible results together with your team in 4-8 weeks.
If you are committed to transforming your organization so you can start consistently delivering insights with AI and ML, we can start with a 2–3 week discovery phase to identify the top challenges, design the ML platform solution, draft the MLOps process, and create an implementation roadmap.