Infrastructure-as-code starter kit
Grid Dynamics has developed and released a cloud-native starter kit for Google Cloud that accelerates the provision of a Machine Learning Platform with end-to-end capabilities, including feature catalog, model repository and CI/CD.
Introduce experiments tracking
A managed Machine Learning Platform on Google Cloud means you have a unified portal to manage experiments with the ability to track model-specific parameters, compare model versions and visualize model key attributes.
Production-ready Machine Learning Platform
Our Machine Learning Platform Starter Kit for Google Cloud provides a full spectrum of ML capabilities: feature store and model repository, experiments tracking, serving layer for batch and real-time inference, model CI/CD.
The platform starter kit leverages Vertex AI for model development and testing, and provides the environment for runtime.
- Build an ML Platform from scratch
- Upgrade an existing ML Platform
- Introduce new technical features
- Migrate to the cloud
- Reduce time-to-market
- Adopt MLOps best practices
The goal of using Machine Learning for smart manufacturing is to identify opportunities to improve industrial operations and OEE at any phase of the manufacturing process. A Machine Learning Platform empowers manufacturers to reduce costs, improve quality, detect anomalies, predict maintenance needs, accelerate factory floor processes, and achieve sustainability objectives.
Retailers in today's digital age need to have their finger on the pulse of consumer behavior to stay ahead of the competition. Leveraging Machine Learning, retailers are more empowered to provide personalized customer experiences, optimize inventory and order management processes, predict demand for products, optimize pricing and marketing campaigns, and a number of other decision-support applications.
Technology and media companies can leverage Machine Learning to reveal diagnostic, predictive and prescriptive insights that help with content optimization, distribution strategies, marketing spend optimization, customer personalization, churn mitigation, and overall process efficiency by increasing the agility of data science teams.
Machine Learning has become critical in the financial services industry for process automation, document analysis, portfolio management, algorithmic trading, risk management, fraud detection, money laundering prevention, customer experience personalization, pricing and product optimization, and more. In a nutshell, Machine Learning can analyze millions of data sets within a short time to improve financial services outcomes.
Machine Learning can save pharma and medicine hundreds of billions of dollars annually because of greater efficiencies in research and development and clinical trials, better insights for decision-making and more personalized customer/patient experiences, and innovative tools that can help consumers, physicians, regulators and insurers make informed decisions.
Benefits of a Machine Learning Platform in the cloud
With Infrastructure-as-code, you get to focus on building business critical applications, not on infrastructure provisioning and management.
Fully managed by Google Cloud services and Terraform, infrastructure provisioning takes days, not months.
The Machine Learning Platform's scalability and modular architecture enables flexible extension of new capabilities with Dataiku or other cloud products.
How It Works
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