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Вернуться к AI Workflow: Enterprise Model Deployment

Отзывы учащихся о курсе AI Workflow: Enterprise Model Deployment от партнера IBM

4.1
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Оценки: 46

О курсе

This is the fifth course in the IBM AI Enterprise Workflow Certification specialization.   You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises.  Apache Spark is a very commonly used framework for running machine learning models.  Best practices for using Spark will be covered in this course.  Best practices for data manipulation, model training, and model tuning will also be covered.  The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies.   By the end of this course you will be able to: 1.  Use Apache Spark's RDDs, dataframes, and a pipeline 2.  Employ spark-submit scripts to interface with Spark environments 3.  Explain how collaborative filtering and content-based filtering work 4.  Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5.  Analyze hyperparameters in machine learning models on Apache Spark 6.  Deploy machine learning algorithms using the Apache Spark machine learning interface 7.  Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process....

Лучшие рецензии

NM

7 июля 2020 г.

Dear Team,

Namaste !!

Well ...Excellent Course ..

Thanks for All Support ...

AA

29 мая 2020 г.

Very nice overview of recommendation systems and deployment to spark for scaling.

Фильтр по:

1–9 из 9 отзывов о курсе AI Workflow: Enterprise Model Deployment

автор: Lam C V D

•

29 авг. 2020 г.

Please take note these courses assumes you have the skills like Scala, Dockers, Python etc. The practice is one lab ungraded

автор: Akhil A

•

29 мая 2020 г.

Very nice overview of recommendation systems and deployment to spark for scaling.

автор: Neela M

•

8 июля 2020 г.

Dear Team,

Namaste !!

Well ...Excellent Course ..

Thanks for All Support ...

автор: Бибик М В

•

12 апр. 2021 г.

very good course, i am find a lot of interesting things

автор: Jovane A P

•

14 авг. 2020 г.

Good content and well explained tutorials!

автор: Yi H

•

26 дек. 2020 г.

great examples and real-world case

автор: S.E

•

18 янв. 2021 г.

Nice to finally see the deployment stage but I think I'd probably skip the Watson studios/python part since it just adds to the complexity without adding much value. keep it simple.

автор: Tracy P

•

21 мая 2020 г.

Missing data files, errors in course content, disconnects between stated goal and project files, typos, etc. A very poor showing for a company trying to create an image of quality.

автор: Ashwini S

•

19 авг. 2020 г.

Os.path: never worked.

Many things are high level things. found this course really boring.

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