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Why do the majority of Data Science projects never make it to production?

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Itai Yaffe
Itai Yaffe

María de la Fuente (Solutions Architect Manager for IMEA) @ Databricks While most companies understand the value creation of leveraging data and are taking on board an AI strategy, only 13% of the data science projects make it to production successfully. Besides the well-known skills gap in the market, we need to level up our end-to-end approach and cover all aspects involved when working with AI. In this session, we will discuss the main obstacles to overcome and how we can avoid the major pitfalls to ensure our data science journey becomes successful.

Data & Analytics
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Why the majority of Data Science projects never make it to production?
María de la Fuente Solutions Architect Manager – Israel Middle East &Africa @Databricks María de la Fuente | LinkedIn
AI is poised to change the world $3.9T Projected Business value creation by AI in 2022 And most leaders agree 83% CEOs say AI is a strategic priority But AI doesn’t make it out the door at most companies Of Data Science initiatives never make it to production 87%
Q: Why are these projects struggling? A: It is mainly because of reliability, performance and lack of ML end-to- end tracking mechanism.
ML Code Configuration Data Collection Data Verification Feature Extraction Machine Resource Management Analysis Tools Process Management Tools Serving Infrastructure Monitoring “Hidden Technical Debt in Machine Learning Systems,” Google NIPS 2015 Only a small fraction of real-world ML systems is composed of the ML code, as shown by the small red box in the middle. The required surrounding infrastructure is vast and complex. Hardest Part of ML isn’t ML, it’s Data
How ML Workflow Works?
A typical Machine Learning workflow Data Preparation Feature Engineering Model Training Model Evaluation Model Deployment Model Tuning Model Consumption Data Ingestion Users Data & ML Engineers
Data Scientists time is valuable Source: CrowdFlower Data Science Report
Data Scientists time is valuable Source: CrowdFlower Data Science Report
Data Lake The data is not ready for data science & ML The majority of these projects are failing due to unreliable data! Data Science & ML • Recommendation Engines • Risk, Fraud Detection • IoT & Predictive Maintenance • Genomics & DNA Sequencing
ML Lifecycle is Manual, Inconsistent and Disconnected ● Ad hoc approach to track experiments ● Very hard to reproduce experiments Prep Data ● Multiple tightly coupled deployment options ● Different monitoring approach for each framework Build Model Deploy Model ● Low level integrations for Data and ML ● Difficult to track data used for a model
Nothing last forever ”Change is the only constant in life¨-Heraclitus, Greek Philosopher 12 One of the main assumptions when creating a model is that future data will be similar to past data used to build the model HOWEVER, Models exists in a dynamic and continually changing environment, when this environment change, the performance of the model will change too
This means…Model Drifting is expected! 13 ML Models will lose their predictive power over time CONCEPT DRIFT  properties of the dependent variable(s) change(s) DATA DRIFT  properties of the independent variable(s) change(s)
ML Lifecycle and Challenges Delta Tuning Model Mgmt Raw Data ETL Train Featurize Score/Serve Batch + Realtime Monitor Alert, Debug Deploy AutoML, Hyper-p. search Experiment Tracking Remote Cloud Execution Project Mgmt (scale teams) Model Exchange Data Drift Model Drift Orchestration (Airflow, Jobs) A/B Testing CI/CD/Jenkins push to prod Feature Repository Lifecycle mgmt. Retrain Update Features Production Logs Zoo of Ecosystem Frameworks Collaboration Scale Governance
Q: How we are going to solve these problems?
MLOps: What, why, how? WHAT: Set of practices for collaboration and communication between data scientists and operations professionals WHY: Aims to improve the delivery of machine learning models by combining the processes of design, development, testing, and delivery into a singular process. ● Shortening development cycles, and as a result, decreasing time to market ● Improving collaboration between teams across all levels of technical expertise ● Increasing reliability, performance, scalability, and security of ML systems ● Streamlining operational and governance processes ● Increasing return on investment of ML projects
MLOps vs DevOps SAME SAME… when it comes to continuous integration of source control, unit testing, integration testing, and continuous delivery of the software module or the package …BUT DIFFERENT Continuous Integration (CI) is no longer only about testing and validating code and components, but also testing and validating data, data schemas, and models Continuous Deployment (CD) is no longer about a single software package or service, but a system (an ML training pipeline) that should automatically deploy another service (model prediction service) or roll back changes from a model Continuous Testing (CT) is a new property, unique to ML systems, that’s concerned with automatically retraining and serving the models
Tactics for Successful & Scalable ML in production ● Align business needs & ML Objectives ● Involve right personas ● Lean into the cloud ● Break the silos & support cross-colaboration ● Architect with operations in mind ● Invest & Leverage MLOps
How do we bring it together at Databricks?
End-to-End Data Science and ML on AutoML End-to-End ML Lifecycle ML Runtime and Environments Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open, pluggable architecture
High level Architecture Unified Analytics Platform Data Science, Model Training, Test and Selection APIs Jobs Models Notebooks Dashboards ML Runtime Databricks Runtime BI Tool Connectors Model Deployment& Monitoring to the cloud... to the edge... ETL / Data Processing Bronze Gold DB Connect Tracking Projects Models End to end ML lifecycle Registry Connectors and APIs for a wide variety of differentsources... File DB/DW
Thanks!

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Why do the majority of Data Science projects never make it to production?

  • 1. Why the majority of Data Science projects never make it to production?
  • 2. María de la Fuente Solutions Architect Manager – Israel Middle East &Africa @Databricks María de la Fuente | LinkedIn
  • 3. AI is poised to change the world $3.9T Projected Business value creation by AI in 2022 And most leaders agree 83% CEOs say AI is a strategic priority But AI doesn’t make it out the door at most companies Of Data Science initiatives never make it to production 87%
  • 4. Q: Why are these projects struggling? A: It is mainly because of reliability, performance and lack of ML end-to- end tracking mechanism.
  • 5. ML Code Configuration Data Collection Data Verification Feature Extraction Machine Resource Management Analysis Tools Process Management Tools Serving Infrastructure Monitoring “Hidden Technical Debt in Machine Learning Systems,” Google NIPS 2015 Only a small fraction of real-world ML systems is composed of the ML code, as shown by the small red box in the middle. The required surrounding infrastructure is vast and complex. Hardest Part of ML isn’t ML, it’s Data
  • 7. A typical Machine Learning workflow Data Preparation Feature Engineering Model Training Model Evaluation Model Deployment Model Tuning Model Consumption Data Ingestion Users Data & ML Engineers
  • 8. Data Scientists time is valuable Source: CrowdFlower Data Science Report
  • 9. Data Scientists time is valuable Source: CrowdFlower Data Science Report
  • 10. Data Lake The data is not ready for data science & ML The majority of these projects are failing due to unreliable data! Data Science & ML • Recommendation Engines • Risk, Fraud Detection • IoT & Predictive Maintenance • Genomics & DNA Sequencing
  • 11. ML Lifecycle is Manual, Inconsistent and Disconnected ● Ad hoc approach to track experiments ● Very hard to reproduce experiments Prep Data ● Multiple tightly coupled deployment options ● Different monitoring approach for each framework Build Model Deploy Model ● Low level integrations for Data and ML ● Difficult to track data used for a model
  • 12. Nothing last forever ”Change is the only constant in life¨-Heraclitus, Greek Philosopher 12 One of the main assumptions when creating a model is that future data will be similar to past data used to build the model HOWEVER, Models exists in a dynamic and continually changing environment, when this environment change, the performance of the model will change too
  • 13. This means…Model Drifting is expected! 13 ML Models will lose their predictive power over time CONCEPT DRIFT  properties of the dependent variable(s) change(s) DATA DRIFT  properties of the independent variable(s) change(s)
  • 14. ML Lifecycle and Challenges Delta Tuning Model Mgmt Raw Data ETL Train Featurize Score/Serve Batch + Realtime Monitor Alert, Debug Deploy AutoML, Hyper-p. search Experiment Tracking Remote Cloud Execution Project Mgmt (scale teams) Model Exchange Data Drift Model Drift Orchestration (Airflow, Jobs) A/B Testing CI/CD/Jenkins push to prod Feature Repository Lifecycle mgmt. Retrain Update Features Production Logs Zoo of Ecosystem Frameworks Collaboration Scale Governance
  • 15. Q: How we are going to solve these problems?
  • 16. MLOps: What, why, how? WHAT: Set of practices for collaboration and communication between data scientists and operations professionals WHY: Aims to improve the delivery of machine learning models by combining the processes of design, development, testing, and delivery into a singular process. ● Shortening development cycles, and as a result, decreasing time to market ● Improving collaboration between teams across all levels of technical expertise ● Increasing reliability, performance, scalability, and security of ML systems ● Streamlining operational and governance processes ● Increasing return on investment of ML projects
  • 17. MLOps vs DevOps SAME SAME… when it comes to continuous integration of source control, unit testing, integration testing, and continuous delivery of the software module or the package …BUT DIFFERENT Continuous Integration (CI) is no longer only about testing and validating code and components, but also testing and validating data, data schemas, and models Continuous Deployment (CD) is no longer about a single software package or service, but a system (an ML training pipeline) that should automatically deploy another service (model prediction service) or roll back changes from a model Continuous Testing (CT) is a new property, unique to ML systems, that’s concerned with automatically retraining and serving the models
  • 18. Tactics for Successful & Scalable ML in production ● Align business needs & ML Objectives ● Involve right personas ● Lean into the cloud ● Break the silos & support cross-colaboration ● Architect with operations in mind ● Invest & Leverage MLOps
  • 19. How do we bring it together at Databricks?
  • 20. End-to-End Data Science and ML on AutoML End-to-End ML Lifecycle ML Runtime and Environments Batch Scoring Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open, pluggable architecture
  • 21. High level Architecture Unified Analytics Platform Data Science, Model Training, Test and Selection APIs Jobs Models Notebooks Dashboards ML Runtime Databricks Runtime BI Tool Connectors Model Deployment& Monitoring to the cloud... to the edge... ETL / Data Processing Bronze Gold DB Connect Tracking Projects Models End to end ML lifecycle Registry Connectors and APIs for a wide variety of differentsources... File DB/DW
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