Production ML Systems
Acquire the ability to differentiate between static and dynamic training and inference, manage model dependencies, establish distributed training for defect tolerance and replication, and generate exportable models.
Machine Learning,Google Cloud,dynamic training
Description for Production ML Systems
Level: Advanced
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Google Cloud
Duration: 18 hours (approximately)
Schedule: Flexible
Pricing for Production ML Systems
Use Cases for Production ML Systems
FAQs for Production ML Systems
Reviews for Production ML Systems
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Production ML Systems
This learning path provides a thorough overview of generative AI. This specialization delves into the ethical considerations that are essential for the responsible development and deployment of AI, as well as the foundations of large language models (LLMs) and their diverse applications.
Learn how to use Gemini for Google Workspace to boost productivity and efficiency in Gmail through its generative AI features.
Learn to describe and implement various machine learning algorithms in Python, including classification and regression techniques, and evaluate their performance using appropriate metrics.
Learn fundamental machine learning principles, including K nearest neighbor, linear regression, and model analysis, with prerequisites of Python programming and basic mathematics.
Gain foundational knowledge of Linear Algebra and Machine Learning models, explore the scalability of SparkML and Scikit-Learn, and gain practical experience by adjusting models and analyzing vibration sensor data in a real-world IoT example.
Learn to leverage Google Cloud's data-to-AI tools, generative AI capabilities, and Vertex AI for comprehensive ML model development.
Gain comprehensive knowledge of ML pipelines, model persistence, Spark applications, data engineering, and hands-on experience with Spark SQL and SparkML for regression, classification, and clustering.
Explore the functionality, practical applications, limitations, and advancements of diffusion models, including their text-to-image applications.
This course explores enterprise machine learning applications, assesses the viability of ML use cases, and addresses the prerequisites, data characteristics, and critical factors for developing and managing ML models.
The course introduces Google Cloud fundamentals for transforming business models with data, ML, and AI, targeting those interested in cloud AI/ML impacts on business without requiring prior experience, and excludes hands-on technical training.
Featured Tools
Explore the use of generative AI tools to enhance data preparation, querying, and machine learning model development in data science workflows through hands-on projects.
This course will provide you with an understanding of the technical underpinnings and essential terminology associated with generative artificial intelligence (AI).
Explore the fundamentals, applications, ethical implications, and future trends of generative AI in human resources.
Generative AI for Your Benefit. Utilize Generative AI to develop and instruct personalized assistants.
Developing a Strategic Advantage through the Mastery of Generative AI. Leverage the transformative potential of Generative AI to empower your leadership suite.