Hiring Apache Spark ML developers? Then you should know!
How and where is Apache Spark ML used?
- Customer Segmentation: Identify customer segments based on behavior
- Fraud Detection: Detect fraudulent activities in real-time
- Sentiment Analysis: Analyze sentiment of customer reviews
- Recommendation Engine: Provide personalized recommendations
- Image Recognition: Identify objects in images
- Anomaly Detection: Detect unusual patterns in data
- Churn Prediction: Predict customer churn likelihood
- Clickstream Analysis: Analyze user behavior on websites
- Predictive Maintenance: Predict when equipment needs maintenance
- Healthcare Analytics: Analyze patient data for insights
Compare Junior, Middle, Senior, and Expert/Team Lead Apache Spark ML Developer roles
Seniority Name | Years of experience | Responsibilities and Activities | Average salary (USD/year) |
---|---|---|---|
Junior | 0-2 years |
| $60,000 |
Middle | 3-5 years |
| $80,000 |
Senior | 6-8 years |
| $100,000 |
Expert/Team Lead | 9+ years |
| $120,000 |
Quick Facts about Apache Spark ML.
- Apache Spark ML was created in 2014 by Matei Zaharia.
- It is widely used in projects involving machine learning algorithms.
- The entry threshold for Apache Spark ML is relatively low.
- Apache Hadoop is one of the most popular technologies related to it.
- Fun Fact: Apache Spark ML can process data up to 100 times faster than Hadoop.
TOP Apache Spark ML Related Technologies
- Spark NLP
- TensorFlow
- Keras
- PyTorch
(John Snow Labs, 2017)
(Google Brain Team, 2015)
(François Chollet, 2015)
(Facebook AI Research lab, 2016)
What are top Apache Spark ML instruments and tools?
- MLlib: Apache’s ML library
- SparkNLP: John Snow Labs, 2019
- MLflow: Databricks, 2018
- Spark-TSNE: Liang-Wei, 2015
- BigDL: Intel, 2016
- H2O Sparkling Water: H2O.ai, 2017
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