This GCP-AMLTF: Advanced Machine Learning with TensorFlow on Google Cloud course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build recommendation systems
and scalable, accurate, and production-ready models for structured data, image data, time series, and natural language text.
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Google Cloud Machine Learning and Artificial Intelligence certification training.
Learn to implement machine learning models and deploy the latest artificial intelligence tools with Google Cloud.
Did you know that the adoption of machine learning results in 2x more data-driven decisions, 5x faster decision-making, and 3x faster execution?
Organizations around the world are seeking to leverage the power of AI and rightly so. The smart applications of Artificial Intelligence enable organizations to improve, to scale, and to accelerate the decision-making process across most business functions, so as to work both more efficiently and more effectively. It can also open up new avenues and new revenue streams, providing the organization with an additional competitive edge. In short, many believe (as we do) that the enterprises that invest in building industry-specific AI solutions today are positioning themselves to be the global economic leaders of tomorrow.
Learn how to implement the latest machine learning and artificial intelligence technology by exploring Google Cloud training on BigQuery, TensorFlow, Cloud Vision, Natural Language API, and more.
Google Cloud Machine Learning and Artificial Intelligence certification training.
Learn to implement machine learning models and deploy the latest artificial intelligence tools with Google Cloud.
Did you know that the adoption of machine learning results in 2x more data-driven decisions, 5x faster decision-making, and 3x faster execution?
Organizations around the world are seeking to leverage the power of AI and rightly so. The smart applications of Artificial Intelligence enable organizations to improve, to scale, and to accelerate the decision-making process across most business functions, so as to work both more efficiently and more effectively. It can also open up new avenues and new revenue streams, providing the organization with an additional competitive edge. In short, many believe (as we do) that the enterprises that invest in building industry-specific AI solutions today are positioning themselves to be the global economic leaders of tomorrow.
Learn how to implement the latest machine learning and artificial intelligence technology by exploring Google Cloud training on BigQuery, TensorFlow, Cloud Vision, Natural Language API, and more.
Google Cloud AI and machine learning solutions.
Put the best of Google’s artificial intelligence to work helping your business run faster and smoother, while finding new ways to delight your customers. Google Cloud AI and Machine Learning training consists of 2 paths:
Data Scientist/Machine Learning Engineer:
A Data Scientist models and analyzes key data to continually improve how businesses utilize data. Data Scientists aim to make accurate predictions about the future using in-depth data modeling and deep learning.
Contact Center Engineer:
Learn how to design, develop, and deploy customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You’ll also learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale.
Take the next step in your AI and Machine Learning journey with the Google Professional Machine Learning Engineer certification exam.
The Google Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques.
The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.
Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.
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Trainocate: A Global Leader in Technology, Business, and People Development
In a world that never stands still, we help you move forward.
With over 30 years of experience, Trainocate has been empowering individuals and businesses to build the essential skills that drive growth and success. Whether it’s professionals advancing in their careers or organizations transforming their workforce, our training programmes deliver results.
Specializing in cloud computing, data & AI, cybersecurity, and digital leadership, our training is powered by industry-leading technology partners like AWS, Alibaba Cloud, Cisco, EC-Council, Fortinet, Google Cloud, IBM, ISACA, ISC2, Microsoft, NetApp, Nutanix, Oracle, Palo Alto, Splunk, Red Hat, Rocheston, Trend Micro, and VMware.
Trainocate Group operates in 24 countries blending global expertise with localized insights to train the next generation of tech professionals and business leaders.
Trainocate Malaysia, based in KL Eco City, is proud to be an HRD Corp Registered Training Provider and a Yayasan Peneraju ALTI, driving workforce development across Malaysia.
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