Whether it’s for model design, deployment, monitoring, or security, you will use a range of in-demand tools, libraries, languages, and technologies during this program.
Why Choose This MLOps Course?
End-to-end MLOps Lifecycle Mastery
Learn how to efficiently deploy and maintain scalable ML model.
Expert-Led Curriculum
360° MLOps course designed and taught by FAANG+ experts to help you effectively deploy ML models
Hands-on Expertise
Move beyond theory with practical skills in designing, implementing, and putting models to production with AWS.
Stay Current, Stay Relevant
The field of ML and AI moves fast. Our course, grounded in the latest research and industry demands, keeps you at the forefront.
Mock interviews with FAANG+ Data Scientists
Get ahead of the competition - showcase your expertise with a comprehensive portfolio of high-end in-demand projects.
Live, Interactive Expert Sessions
Engage with AI pioneers and draw insights from their vast experience.
Build successful interview strategies and practice answering the toughest security engineering interview questions
Extensive coverage of interview relevant Security Engineering topics such as Applied Cryptography, Application, Network and Cloud Security and more
Get mentored by our highly experienced instructors working as active hiring managers and at FAANG+ companies and know exactly what it takes to ace tech and managerial interviews.
Course Highlights
(Program duration: 9 weeks)
Advanced Data Handling Techniques
Learn sophisticated methods for data storage, versioning, and utilization of feature stores.
Practical Skills in Model Training and Deployment:
Develop expertise in hyperparameter tuning, model packaging, containerization, and deployment using modern CI/CD practices.
Kubernetes and Cloud Platforms Utilization
Gain hands-on experience in using Kubernetes for orchestrating containers and leveraging cloud platforms for scalable ML solutions.
Monitoring, Security, and Governance in ML
Acquire skills in monitoring ML models, managing data drift, and ensuring robust security and governance practices in ML pipelines.
Eligibility Criteria
This program is best suited for Machine Learning Engineers, Applied Scientists, and Data Scientists looking to revisit or master MLOps skills for pushing large-scale ML models to production.
Prerequisites
Knowledge of Python or any other scripting language.
Prior experience in basic or classical ML modeling/prototyping
Comfortable with data processing and deep learning concepts.
Train with MLOps Industry Practitioners
Our highly experienced instructors are active ML Professionals at FAANG+ companies, bringing a treasure trove of knowledge and industry expertise.
Jude Safo
Former ML Engineer
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Naveen Neppalli
Former Head of Engineering & Machine learning
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Parivesh Priye
Applied Scientist
FAANG+ Leader
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James Vance
Machine Learning Engineer
FAANG+ Leader
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Career Impact: What Our Alumni Are Saying
Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.
Siva Karthik Gade
Machine Learning Engineer
Placed at:
IK offers high-quality study material, knowledgeable and patient instructors working at industry-leading companies, well-paced live classes + tests + review sessions, always available technical coaches. IK brings together people with the same ambition on their platform, Uplevel, to guide and inspire each other.
Humberto Gonzales Granda
Machine Learning Engineer
Placed at:
Interview Kickstart's dedicated team of instructors and coaches provided exceptional support and mentorship. Their extensive knowledge and experience in the tech industry is evident in the program's meticulously crafted curriculum. The diverse range of topics covered, including data structures, algorithms, and systems design, was nothing short of impressive, ensuring that I was well-equipped to tackle any challenge that came my way. One standout feature that sets Interview Kickstart apart is the personalized attention provided to each participant. The program's well-structured curriculum, passionate instructors, and unparalleled support make it a game-changer that can unlock your true potential.
Mike Kane
Lead Data Engineer
Placed at:
For many working professionals, going through examples and different perspectives are very valuable…. Interview Kickstart was great because its structure helped me understand each problem in my interview. The high sense of comradery in Discord was also great! I had a study group with other people in my cohort and felt the engagement was much stronger than in an academic setting.
Davide Testuggine
Software Engineer
Placed at:
What turned me to Soham’s course is the way he talked about the course as not a substitute for hard work, not a “cheat sheet” of questions but a way to actually get good at algorithms, through a lot of perspiration. The course is very intense…practice, practice, practice. And it works!.... All that practice had a long-lasting effect on my ability as a software engineer. I am simply faster at coding than I ever was…. I can keep focused on the idea if the implementation takes a few minutes as I don't get lost on implementation details anymore, so the productivity increase I experienced is greater than just the delta in time for the implementation itself.
Students who chose to uplevel with IK got placed at
Siva Karthik Gade
SDE, Machine Learning
Sai Marapa Reddy
SWE, Machine Learning
Safir Merchant
SWE, Machine Learning
Jameson Merkow
Principal AI Engineer
Sayan Banerjee
Data Scientist II
Mike Kane
Lead Data Engineer, Analytics
Akshay Lodha
Data Engineering & Analytics
Anju Mercian
Data Engineering Consultant
Alokkumar Roy
Data Engineer
20,000+
Tech professionals trained
$1.2M
Highest offer received by an IK alum
66.5%
Average salary hike received by alums
MLOps Detailed Curriculum
Design Considerations
Week 1
1
ML Model Lifecycle and MLOps
2
Data Management
3
Model Training
4
Model Deployment and Inference
5
Monitoring and Iterative Improvement
6
Security and Governance
7
Introduction to AWS/Cloud Computing
Data Management
Week 2
1
Data Storage Patterns - LFS, NFS, Cloud, Databases
2
Data Lakes and Feature Stores
3
Data Versioning and Tracking
4
Feature Stores
5
Data Pipelining
6
Tools/ Language Used: Git, Data Version Control (DVC), Git LFS, Feast, Airflow
Model Training -1
Week 3
1
Large Scale/Distributed Hyperparameter Tuning and
2
Experiment Tracking
3
Tools/ Language Used:skopt, Raytune, MLflow Tracking
Model Training -2
Week 4
1
Project Packaging and Model Versioning
2
Distributed Training
3
Tools/ Language Used:MLflow Model Registry, TensorFlow Distributed, Distributed Data-Parallel and FSDP
Model Deployment and Inference - 1
Week 5
1
Containerization with Docker
2
CI/CD
3
Model Deployment Considerations
4
Model Preparation for Deployment
5
Batch Inference/Deployment
6
RESTful APIs based Real Time Inference/Deployment
7
Tools/ Language Used:Pickle, Tensorflow, Pytorch, Airflow, FastAPI, Swagger