Amazon Sage Maker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.
DurationAmazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML). With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.
Orchestrating workflows across each step of the machine learning process (e.g., exploring and preparing data, experimenting with different algorithms and parameters, training and tuning models, and deploying models to production) can take months of coding.
The Machine Learning Pipeline on AWS course explores how to use the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment.
Students will learn about each phase of the process pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays.
By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of statistics will be helpful.
Trainocate, an AWS Authorized Training Partner as well as the AWS Global Training Partner of the Year 2022, is trusted by AWS to offer, deliver, and/or incorporate official AWS training, including classroom and digital offerings. Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS training options for learners of all levels.
Skills Covered:
Select and justify the appropriate ML approach for a given business problem
Use the ML pipeline to solve a specific business problem
Train, evaluate, deploy, and tune an ML model using Amazon SageMaker
Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS
Apply machine learning to a real-life business problem after the course is complete
Worldwide Leader in Technology, Business and People Training. From Asia To The World. Trainocate Malaysia is the new and exciting development of the 25-year old establishment – Global Knowledge.
The name Trainocate is inspired from the term “Training Advocate” which resonates the feeling of continuous learning, something that each member of the Trainocate team carries with them.
We are in the business of empowering work forces in the Asia-Pacific region by maximising their capabilities through knowledge acquirement. This not only helps with improving work performance but also, explores untapped potential within each individual.
We deliver training programs and certifications that fall in the area of Technology, Business and People Skills. 25 years of experience in this industry equates to 25 years of bettering ourselves to ensure we deliver only the best training programs to our loyal clients both in the area of Technology as well as People Skills.
Machine Learning & Deep Learning course is offered by Academy Adelphi Worldwide Edu Sdn Bhd. Our in-house training option enables you to select the mix of participants to ensure optimum results and promote team spirit.
Machine Learning involves using algorithms to build a good and useful approximation to data and then decide or predict. However, machine learning can be very complex and most people need to be trained to program or have the time to do so.
As a subset of artificial intelligence (AI), machine learning (ML) is a field of computer science that focuses on the analysis and interpretation of patterns and structures in data to achieve learning, reasoning and decision making outside of human interaction.
Machine Learning is revolutionizing the world by allowing computers to learn as they progress forward with large data sets, overwriting overcoming all programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve.
This is an introductory programme on machine learning using scikit-learn and scipy for structured data, and common text processing methods. It has been designed for participants who aim to understand the possible applications of these techniques in their respective organisations.
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