Master Machine Learning Ops fundamentals for successful production deployment! Learn the approach and techniques to optimize ML models. Read more.
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Who this course is for:
- Beginner who wants to start their journey in ML in Production
- Starting point for Data Scientists, Data Engineers, ML Engineers, MLOps Engineers, Data Product Managers, Engineering Leader
What you’ll learn:Â
- Understand the approach to ML to Production
- Understand the fundamentals of MLOps in Production
- Understand MLOps as a process – From Business Discussions – ML in Production
- Evaluation of different types of tools – Make sense of plethora of tools
- Understand different job roles and their future roadmaps
Requirements:Â
- Basic understanding of ML algorithms
Are you looking to start your journey in ML in production? Are you confused with so many tools? Are you confused about where to start your journey?
Did you know more than 50% of people discontinue their journey in ML in production because they feel overwhelmed.
Our comprehensive course on MLOps in production is designed to help you do just that to teach you the proper approach to ML in production.
According to the BCGs report, the pioneers of AI at scale—the companies that have scaled AI across the business and achieved meaningful value from their investments—typically dedicate 10% of their AI investment to algorithms, 20% to technologies, and 70% to embedding AI into business processes and agile ways of working.
Why give so much importance to the tools? Rather emphasis should be given to the process.
This course is suitable for anyone looking to advance their machine learning skills, including Data engineers, ML engineers, Data Scientists, MLOps platform engineers, and MLOps Engineers. By the end of the course, you’ll have a deep understanding of the major root causes of failure in ML in production, the fundamentals of MLOps, MLOps as a process and the future roadmap in ML in production.
I have been working along with industry experts and industry mentors for the past year to understand the root causes in ML in production.
Our Promise to You
By the end of this course, you will have learned the proper approach to ML in production.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today!
Course Curriculum
| Section 1 - Production Machine Learning 101 | |||
| Course Introduction | 00:00:00 | ||
| Root Cause Of Failure | 00:00:00 | ||
| Fundamentals - ML System VS MLL Code | 00:00:00 | ||
| Fundamentals - ML Research VS ML Production | 00:00:00 | ||
| Fundamentals - DevOps VS MLOps | 00:00:00 | ||
| Fundamentals - MLOps Vs LLMOps | 00:00:00 | ||
| MLOps As Process | 00:00:00 | ||
| MLOps Process - Detailed | 00:00:00 | ||
| Tools AIIA | 00:00:00 | ||
| Levels of Frameworks | 00:00:00 | ||
| Industry Of Production ML | 00:00:00 | ||
| Career Prospects In Production MLL | 00:00:00 | ||
| Conclusion | 00:00:00 | ||
About This Course
Who this course is for:
- Beginner who wants to start their journey in ML in Production
- Starting point for Data Scientists, Data Engineers, ML Engineers, MLOps Engineers, Data Product Managers, Engineering Leader
What you’ll learn:Â
- Understand the approach to ML to Production
- Understand the fundamentals of MLOps in Production
- Understand MLOps as a process – From Business Discussions – ML in Production
- Evaluation of different types of tools – Make sense of plethora of tools
- Understand different job roles and their future roadmaps
Requirements:Â
- Basic understanding of ML algorithms
Are you looking to start your journey in ML in production? Are you confused with so many tools? Are you confused about where to start your journey?
Did you know more than 50% of people discontinue their journey in ML in production because they feel overwhelmed.
Our comprehensive course on MLOps in production is designed to help you do just that to teach you the proper approach to ML in production.
According to the BCGs report, the pioneers of AI at scale—the companies that have scaled AI across the business and achieved meaningful value from their investments—typically dedicate 10% of their AI investment to algorithms, 20% to technologies, and 70% to embedding AI into business processes and agile ways of working.
Why give so much importance to the tools? Rather emphasis should be given to the process.
This course is suitable for anyone looking to advance their machine learning skills, including Data engineers, ML engineers, Data Scientists, MLOps platform engineers, and MLOps Engineers. By the end of the course, you’ll have a deep understanding of the major root causes of failure in ML in production, the fundamentals of MLOps, MLOps as a process and the future roadmap in ML in production.
I have been working along with industry experts and industry mentors for the past year to understand the root causes in ML in production.
Our Promise to You
By the end of this course, you will have learned the proper approach to ML in production.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today!
Course Curriculum
| Section 1 - Production Machine Learning 101 | |||
| Course Introduction | 00:00:00 | ||
| Root Cause Of Failure | 00:00:00 | ||
| Fundamentals - ML System VS MLL Code | 00:00:00 | ||
| Fundamentals - ML Research VS ML Production | 00:00:00 | ||
| Fundamentals - DevOps VS MLOps | 00:00:00 | ||
| Fundamentals - MLOps Vs LLMOps | 00:00:00 | ||
| MLOps As Process | 00:00:00 | ||
| MLOps Process - Detailed | 00:00:00 | ||
| Tools AIIA | 00:00:00 | ||
| Levels of Frameworks | 00:00:00 | ||
| Industry Of Production ML | 00:00:00 | ||
| Career Prospects In Production MLL | 00:00:00 | ||
| Conclusion | 00:00:00 | ||
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