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 scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text, along with recommendation systems.
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.
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Empower your business with Dialogflow CX’s Advanced AI capabilities.
In this Google Cloud course, learn how to design customer conversations using Contact Center Artificial Intelligence (CCAI). You’ll use Dialogflow CX to create virtual agents and test them using the simulator. Learn to add functionality to access data from external systems, making virtual agents conversationally dynamic. You’ll be introduced to testing methods, connectivity protocols, APIs, environment management, and compliance measures.
Learn best practices for integrating conversational solutions with your existing contact center software and implementing solutions securely and at scale.
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What is machine learning, and what kinds of problems can it solve? Why are neural networks so popular right now? How can you improve data quality and perform exploratory data analysis? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent?
In this GCP-MLGC: Machine Learning on Google Cloud course, you’ll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.
MDEC Digital Up Campaign: Enjoy up to RM2,500 Training Fee Reimbursement when you sign-up and attend this course from 15 July – 30 Sept 2023 (Open to Malaysians, Employed, Unemployed, Fresh Graduates, OR Gig workers aged 21-55 years old).
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This GCP-MLOF: MLOps (Machine Learning Operations) Fundamentals course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production.
Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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In this GCP-MPGC: ML Pipelines on Google Cloud course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata.
Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle.
