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                     Machine Learning In this session
•
Lifecycle Explained Core concepts and components
• The six-step lifecycle workflow
Step by Step in 2027 • Tools, use cases, and challenges
• Careers and trends for 2027
From a business idea to a monitored, production-ready 
model
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The ML lifecycle is the full journey of a model
Introduction
It runs from business problem to monitored production system
Why the lifecycle matters
Skipping steps leads to weak, unreliable, or unused models
In 2027, lifecycles also cover generative AI and governance
A clear lifecycle helps teams plan, build, and improve faster
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Core Concepts
Definition and fundamentals
ML lifecycle Iterative Data first
repeatable stages for building teams loop back as data and model quality depends on data 
and running ML solutions needs change quality
Metrics Collaboration Responsible AI
success is measured by business business, data, engineering, and fairness, privacy, and 
and technical results risk teams work together transparency at every stage
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Key Components
Main elements of the lifecycle
1 2 3 4 5 6
Problem Data Model Deployment Monitoring Governance
definition development
goals, users, and collection, training, tuning, serving models accuracy, drift, documentation, 
success measures cleaning, labeling, and evaluation through APIs, bias, cost, and approvals, 
and feature apps, or batch jobs response time security, and 
creation compliance
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How It Works
Machine learning lifecycle step by step
Define the problem Engineer features Deploy the best 
and agree on and prepare model after testing 
success metrics training data and approval
1 2 3 4 5 6
Collect, clean, and Train, tune, and Monitor, gather 
explore the data evaluate candidate feedback, and 
models retrain regularly
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Tools and Technologies
Popular tools and their purpose
Python, pandas, and SQL data preparation and analysis
scikit-learn, PyTorch, TensorFlow build and train models
MLflow track experiments and manage model versions
Docker and Kubernetes package and run models reliably
Airflow and GitHub Actions automate pipelines and releases
Evidently and Prometheus monitor drift and system health
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Real-World Use Cases
Banking Healthcare
fraud detection and credit risk scoring medical image support and patient risk prediction
Retail Manufacturing
demand forecasting and personalized offers predictive maintenance using sensor data
Telecom Customer service
customer churn prediction and network optimization AI assistants using large language models
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Benefits and Challenges
Benefits Challenges
A clear process reduces rework and project 
Poor or biased data lowers model quality
failure
Steady feedback loops improve model Models drift as real-world conditions 
quality change
Easier audits, teamwork, and knowledge Privacy, security, and regulation add 
sharing requirements
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Career Opportunities and Future Trends
ROLES DEMAND
Data Scientist, ML Engineer, MLOps Strong across finance, health, retail, 
Engineer, Data Analyst and technology
SKILLS TRENDS
Python, SQL, statistics, ML basics, ML Career Generative AI, AI agents, LLMOps, 
and cloud tools Path and responsible AI
SOFT SKILLS OUTLOOK
Communication, problem framing, More automation, with human 
and teamwork judgment still essential
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Summary and Conclusion
01 The ML lifecycle guides a project from idea to impact
02 Good data and clear goals matter most Thank 
03 Deployment is not the end; monitoring continues you
04 Responsible AI and governance belong in every stage Questions and discussion
05 Start with small projects and iterate often
06 Keep learning as tools and practices evolve
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