Machine Learning Using SAS Viya
This program�teaches participants on the integration of machine learning into data pipelines on Google Cloud, emphasizing core skills, practical applications, and productionalization utilizing Vertex AI.
Description for Machine Learning Using SAS Viya
Guidance from Industry Professionals: Acquire knowledge directly from industry experts, obtaining insights into optimal methods and practical implementations of machine learning in data pipelines.
Fundamental Knowledge and Practical Skills: Acquire a comprehensive understanding of machine learning, artificial intelligence, and deep learning while cultivating job-relevant skills through practical projects.
BigQuery and Machine Learning APIs for Unstructured Data: Perform BigQuery commands from notebooks and acquire knowledge on utilizing ML APIs for analyzing unstructured data in cloud contexts.
Develop Machine Learning Models utilizing SQL and Vertex AI AutoML: Develop machine learning models with SQL syntax in BigQuery and without programming via Vertex AI AutoML, hence optimizing the model construction process.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Google Cloud
Duration: 6 hours (approximately)
Schedule: Flexible
Pricing for Machine Learning Using SAS Viya
Use Cases for Machine Learning Using SAS Viya
FAQs for Machine Learning Using SAS Viya
Reviews for Machine Learning Using SAS Viya
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Machine Learning Using SAS Viya
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
Learn how to use AI technologies for personal development and active learning, embrace continuous learning, and cultivate a growth mindset.
This training provides professionals with knowledge and practical advice on AI ethics, compliance issues, and risk management.
Learn the skills necessary to operate, optimize, and implement large language models through practical experience with state-of-the-art LLM architectures and open-source resources.
Develop expertise in the exposure and deployment of large language models via application programming interfaces (APIs), configure server environments, and incorporate natural language processing (NLP) functionalities into applications.
Learn proficiency in the construction, deployment, and safeguarding of large language models at scale, utilizing Rust, Amazon Web Services (AWS), and established DevOps best practices.
This course is dedicated to the setting up of GPU-based environments, the deployment of local large language models (LLMs), and their integration into Python applications utilizing open-source tools.
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
Gain proficiency in the automation of software development processes through the utilization of generative artificial intelligence, AI-assisted programming, MLOps, and Amazon Web Services.
Discover AI terminology, ethical norms, and protocols for responsibly utilizing and citing Generative AI.
Featured Tools
Explore the world of AI-powered language processing by acquiring the skills necessary to construct chatbots, analyze sentiment, and incorporate AI insights into practical applications.
A structured guide to the study of business opportunities in the chatbot space, as well as the comprehension, design, and deployment of chatbots using Watson Assistant.
This program instructs instructors on the ethical and successful integration of AI, while promoting innovation and critical thinking among students.
An extensive study of the applications of AI in marketing, ranging from competitive analysis to content optimization and conversion enhancement.
To address OpenAI Gym challenges and real-world problems, this course offers pragmatic artificial intelligence methods like Genetic Algorithms, Q-Learning, and neural network implementation.