阿里云阿里云智能-产品专家-MaxCompute Data+AI方向
社招全职5年以上地点：北京 | 杭州 | 上海状态：招聘
工作描述
任职要求 1. 行业经验与技术背景：有大数据行业或者项目积累，熟悉国际主流云厂商大数据产品，包括但不限于 BigQuery、Snowflake、Databricks 等技术产品，熟悉开源主流技术产品，包括但不限于 Ray、Daft、Hadoop、Hive、Spark、Flink 等技术，曾负责或者参与大数据相关项目者优先。具备 AI/ML 领域项目经验者优先，熟悉大模型训练数据管线、特征工程、向量数据库等技术栈。5年以上相关领域的工作经验。 2. Data+AI 产品专业能力：深刻理解 Data+AI 融合趋势，具备将大数据处理能力与 AI 应用场景结合的产品设计经验。熟悉主流 AI 框架（PyTorch、TensorFlow）及其数据加载机制，了解 DataFrame在机器学习工作流中的核心作用。熟悉 Python 生态，理解 Pandas、Polars、Ray Data 等工具的技术特点与应用场景，有相关产品设计经验者优先。 3. 技术沉淀与趋势洞察：有深厚的技术沉淀，洞察当下技术趋势，全面理解业务、功能、领域和技术架构，能简化技术语言，从客户价值角度影响技术方案选型。对 AI Infra、MLOps、Data Engineering 交叉领域有深入研究，能够预判大模型时代数据基础设施的演进方向，提前布局 MaxFrame的核心能力。有数据引擎工程研发或算法工程师从业经历者优先； 4. 产品经理软技能：具备优秀的产品经理软技能，包括能够熟练使用产品经…
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包括英文材料
大数据+
https://www.youtube.com/watch?v=bAyrObl7TYE
https://www.youtube.com/watch?v=H4bf_uuMC-g
With all this talk of Big Data, we got Rebecca Tickle to explain just what makes data into Big Data.
BigQuery+
[英文] BigQuery Tutorial
https://www.tutorialspoint.com/bigquery/index.htm
Mastering the GCP tools, especially SQL engines like BigQuery, is critical when it comes to beginning or progressing in a data-oriented career.
Snowflake+
https://www.youtube.com/watch?v=mP3QbYURT9k
Databricks+
https://docs.databricks.com/aws/en/getting-started/
The tutorials in this section introduce core features and guide you through the basics of working with the Databricks platform.
https://www.youtube.com/watch?v=QNdiGZFaUFs
This video will act as an intro to databricks.
Ray+
https://github.com/ray-project/ray
Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
https://www.youtube.com/watch?v=FhXfEXUUQp0
In this video, I'll teach you everything you need to know about Apache Ray!
https://www.youtube.com/watch?v=fMiAyj2kgac
Using powerful machine learning algorithms is easy using Ray.io and Python.
https://www.youtube.com/watch?v=q_aTbb7XeL4
Parallel and Distributed computing sounds scary until you try this fantastic Python library.
Hadoop+
https://www.runoob.com/w3cnote/hadoop-tutorial.html
Hadoop 为庞大的计算机集群提供可靠的、可伸缩的应用层计算和存储支持，它允许使用简单的编程模型跨计算机群集分布式处理大型数据集，并且支持在单台计算机到几千台计算机之间进行扩展。
[英文] Hadoop Tutorial
https://www.tutorialspoint.com/hadoop/index.htm
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.
Hive+
[英文] Hive Tutorial
https://www.tutorialspoint.com/hive/index.htm
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
https://www.youtube.com/watch?v=D4HqQ8-Ja9Y
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
Flink+
https://nightlies.apache.org/flink/flink-docs-release-2.0/docs/learn-flink/overview/
This training presents an introduction to Apache Flink that includes just enough to get you started writing scalable streaming ETL, analytics, and event-driven applications, while leaving out a lot of (ultimately important) details.
https://www.youtube.com/watch?v=WajYe9iA2Uk&list=PLa7VYi0yPIH2GTo3vRtX8w9tgNTTyYSux
Today’s businesses are increasingly software-defined, and their business processes are being automated. Whether it’s orders and shipments, or downloads and clicks, business events can always be streamed. Flink can be used to manipulate, process, and react to these streaming events as they occur.
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
特征工程+
https://www.ibm.com/think/topics/feature-engineering
Feature engineering preprocesses raw data into a machine-readable format. It optimizes ML model performance by transforming and selecting relevant features.
https://www.kaggle.com/learn/feature-engineering
Better features make better models. Discover how to get the most out of your data.
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