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Machine Learning in Real-time

Predicting Taxi Fare in NYC

Big Data LDN

Big Data LDN 2019 | Machine Learning in Real-time: Predicting Taxi Fare in NYC | Adam Jelley

Today, the benefit of Machine Learning is conditioned to its deployment in real-time. In this talk, Adam Jelley, Data Scientist, will explain how to deploy a real-time taxi fare prediction engine to power an Uber-like application. Along the cycle of developing such a project, he will highlight key lessons learned, like understand the problem before building models, do not add features for the sake of features, try as many algorithms as possible, and simplify your pipeline before deployment.

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machine learning real-time machine learning predictive analytics taxi fare prediction nyc taxi data uber-like application

What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Teaches machine learning, which is highly relevant to real-time prediction systems
Develops skills in deploying machine learning models in real-time, which is essential for building robust and responsive applications
Focuses on a practical application of machine learning, which is helpful for learners who want to see how these techniques are used in the real world
Taught by Adam Jelley, a data scientist with experience in deploying machine learning models in real-time
Provides insights into the challenges and considerations involved in deploying machine learning models in real-time, which is valuable for learners who want to avoid common pitfalls

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Learners who complete Machine Learning in Real-time: Predicting Taxi Fare in NYC will develop knowledge and skills that may be useful to these careers:
Data Scientist
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Machine Learning Engineer
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Software Engineer
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Data Analyst
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Business Analyst
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Product Manager
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Quantitative Analyst
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Risk Analyst
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Statistician
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Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical models to improve the efficiency of business operations. This course will help you develop the skills needed to become an Operations Research Analyst, including how to build and deploy machine learning models in real-time. This course can help you get started in a career as an Operations Research Analyst or advance your career to a more senior role.
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Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. This course will help you develop the skills needed to become a Data Engineer, including how to build and deploy machine learning models in real-time. This course can help you get started in a career as a Data Engineer or advance your career to a more senior role.
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Machine Learning Researcher
A Machine Learning Researcher develops new machine learning algorithms and models. This course will help you develop the skills needed to become a Machine Learning Researcher, including how to build and deploy machine learning models in real-time. This course can help you get started in a career as a Machine Learning Researcher or advance your career to a more senior role.
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Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, builds, and deploys artificial intelligence systems. This course will help you develop the skills needed to become an Artificial Intelligence Engineer, including how to build and deploy machine learning models in real-time. This course can help you get started in a career as an Artificial Intelligence Engineer or advance your career to a more senior role.
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Data Scientist Manager
A Data Scientist Manager leads a team of Data Scientists. This course will help you develop the skills needed to become a Data Scientist Manager, including how to build and deploy machine learning models in real-time. This course can help you get started in a career as a Data Scientist Manager or advance your career to a more senior role.
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Machine Learning Architect
A Machine Learning Architect designs, builds, and deploys machine learning systems. This course will help you develop the skills needed to become a Machine Learning Architect, including how to build and deploy machine learning models in real-time. This course can help you get started in a career as a Machine Learning Architect or advance your career to a more senior role.
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Course image
Time
23 hours
Level
Intermediate
Via
Pluralsight
Instructor
Big Data LDN
Language
English
This course belongs to a collection called Big Data LDN 2019 . It's comprised of 101 courses:
  1. The Database Is Only Half Done
  2. Your AI Is Wrong! Lessons from Decision-making with Imperfect AI
  3. Multiparadigm Data Science: The Next Frontier in Decision Making
  4. Achieving 100% Adoption of Analytics in the Enterprise
  5. Data Skills: How Mondi Does Digital
  6. Building a Modern Data Architecture for the Data Driven Enterprise
  7. Harnessing the Power of Data to Improve Public Health
  8. Real-time Data Analytics at Scale Using Cloud Services
  9. Quick and Easy Access to Data through a Logical Data Warehouse Environment
  10. The Promise of Data Mastering at Scale
  11. Research vs. Reality in AI: Would You Trust Your Model with Your Life?
  12. Modernise Your IT Infrastructure for AI
  13. Scaling Machine Learning at Holiday Extras
  14. Why Do Some Machine Learning Models Fail?
  15. AI: Lessons Learned from the Front Line
  16. Machine Learning in Real-time: Predicting Taxi Fare in NYC (viewing)
  17. How AI and Automated Machine Learning Will Disrupt the Organisational Alignment of Businesses
  18. Multi-model Powered Machine Learning
  19. ML in Production: Serverless and Painless
  20. Leveraging Data Virtualization to Maximize Advanced Analytics and Machine Learning Potential in Your Data Architecture
  21. Bring the Power of Watson to AWS and Azure: Watson Anywhere Powered by Cloud Pak for Data
  22. Modern Data Warehousing at Scale Using Azure Data Factory
  23. Balancing Performance, Scalability, and High Availability in Your Database Environment
  24. Enabling Microservices for Big Data On-demand in the Cloud
  25. From ETL to ELT: Building a Scalable Data Stack with SQL
  26. Data, Governance, and Freedom
  27. How to Get the Technology Stack Right for Your Business
  28. Data Modelling with Apache Cassandra
  29. How to Orchestrate Your ML Pipelines in Hybrid Cloud
  30. Full Stack Metadata Driven Thinking to Drive Business Agility
  31. Enhance Developer Experience Using OpenShift on Azure
  32. Fast Data for Faster Business
  33. Driving Faster, Smarter Actions on IoT Data with New Real-time Analytics Capabilities
  34. Introduction to Adept and How Big Data and Analytics Is Transforming the Connected Car Ecosystem
  35. Bridge to Cloud
  36. How to Train Your AI Models at Scale
  37. 99% of Your IoT Data Is Worthless, but Which 99%?
  38. Data Engineering in MagicLab: The Birth and Life of an Event
  39. RNA - Event Stream Processing Made Simple
  40. When Data Leads, Transformation Follows - A Look at Qlik's Data Integration Portfolio
  41. Saving Energy in Homes with a Unified Approach to Data and AI
  42. Building Stream Processing Applications with Apache Kafka Using KSQL
  43. Real-world, Real-time Digital Transformation
  44. Machine Data in Industry 4.0: How to Handle It Better
  45. Why CockroachDB in the Kindred Platform?
  46. The Hardest Part of AI & Analytics Is Not AI, It Is Data Management
  47. Effective Cloud Migration Strategies for Your Modern Data Applications
  48. Snowflake? Why Should I Care?
  49. The Future of Business Intelligence Isn't a Tool
  50. AI-driven Retail for the 21st Century
  51. The Next Generation of Data Architecture
  52. Modern Enterprise Data Engineering
  53. Taking Control of User Analytics with Snowplow
  54. Flex Your Data(Ops) Muscles
  55. You Love Big Data, so Does R
  56. Building Predictive Maintenance Solutions
  57. Quickly, Codelessly Connect Any Application to Any REST API
  58. Rethinking Data Warehouse Modernization in the Age of Hybrid Cloud/On-premise Data Warehouses
  59. From Waterfall to Agile – A Data Warehouse Modernisation Story
  60. Freeing up Engineering and Infrastructure Resources to Scale with DataOps
  61. Unit Testing with pytest
  62. Credit Card Fraud – Why the Database Matters
  63. Data Science at SEGA
  64. British Telecom: Data-driven Digital Transformation
  65. Using Cloud Solution Platforms to Drive Business Value and Cost Optimization
  66. Data Analytics & IoT Innovations at Bosch
  67. Converting the CDO to a Profit Centre
  68. Why Self-service Data Analytics Usually Fails & Ideas for Succeeding
  69. Governed Data Discovery
  70. Journey to Create a Great Analytic Culture
  71. Something Amazing for Everyone: Embracing Inclusive Design to Deliver Accessible Experiences
  72. Democratising Data – How We Built a Self-serve Data Platform
  73. Discover the “Why” You Didn't Know Existed
  74. Selecting Your Fantasy Premier League Team with Advanced Analytics
  75. Decouple Storage from Compute
  76. Data Skills: Building a Data Team Fit for a Data-driven Enterprise
  77. Consent, Compliance, and the Impact on Customer Experience
  78. AI & Communication: Meaningful Data from New Sources
  79. Real-time Personalised Customer Interaction at Scale
  80. Data: The Musical, with LW Theatres
  81. Self-service and Tableau Community at Lloyds Banking Group
  82. The Digital Hero’s Journey: Using Big Data to Uncover Business Value and Meaning at Work
  83. Using Location Intelligence to Place Solar Panels in Rwanda
  84. Embedding Intelligence into the Heart of Customer Operations
  85. The Art of Customer Conversations
  86. How to Build a Fully Autonomous Card Fraud Detection System
  87. At Last! The Real-time Customer Experience
  88. Data Privacy Mythbusting
  89. Demystifying Data – What They Don’t Tell You at Data School
  90. Optimizing Your Data Value Chain with Agile Data Management
  91. Data at SEGA Europe
  92. Keep Your Data Lake from Getting Hadooped
  93. Top 10 Privacy Tips & Tricks in Big Data
  94. Data Governance as a Customer Service
  95. 7 AI Quick Win Projects
  96. Delivering Value with Intelligent Data Governance
  97. The Culture Competition – Personalisation vs. Privacy
  98. DataOps for AI and Digital Transformation
  99. Big Data LDN: A GDPR Retrospective
  100. GDPR: A Sword Not a Shield
  101. Drive Business Growth While Satisfying GDPR and CCPA
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Teaches machine learning, which is highly relevant to real-time prediction systems
Develops skills in deploying machine learning models in real-time, which is essential for building robust and responsive applications
Focuses on a practical application of machine learning, which is helpful for learners who want to see how these techniques are used in the real world
Taught by Adam Jelley, a data scientist with experience in deploying machine learning models in real-time
Provides insights into the challenges and considerations involved in deploying machine learning models in real-time, which is valuable for learners who want to avoid common pitfalls
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