Program Overview

The transformative power of AI and ML is undeniable, and it’s not just us who believe so! With industry forecasts predicting that AI and ML will generate an astonishing $4.4 trillion in business value by 2025, there has never been a better time to gear up and equip yourself & your employees with these technologies.

To help your teams stay abreast, Uptut has come up with a comprehensive training program designed to give professionals a competitive edge in mastering MLOps. It includes modules on foundational MLOps principles, practical implementation techniques, integration of AI with cloud infrastructure, and strategies for efficient model deployment in real-world scenarios.

The program enables learners to work on diverse capstone projects (driver demand prediction for optimal food delivery charges, personalized financial advisor using LLM, automated SEO using ChatGPT, etc) that will help them connect theory with practical application.

Our course is designed to be accessible for all skill levels such as developers, engineers, or business leaders like a CIO or CTO. If you're looking to bring AI/ML into your operations quickly and effectively, our experts are here to guide you every step of the way. Reach out to discover how this training can expedite AI/ML implementation in your organization.

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Personalised training curriculum

100% Live Sessions

Lifetime access to recordings and material

Hands-on labs and assignments included

Consulting Support

Delivered by industry experts

Why AI and ML Ops for your business?

Enhanced Operational Efficiency: AI and ML will generate US$4.4 trillion in business value by next year! Upskilling your teams in these areas will help in boosting their productivity and streamline your workflows, significantly boosting overall operational efficiency.
Improved Decision-Making: Training in AI and ML Ops sharpens employees' skills in working with and extracting valuable insights from complex data sets. This not only prepares them to make informed, precise decisions but also empowers them to leverage advanced analytical tools for better business outcomes.
Reduced Operational Risks: This AI and ML Ops training covers risk mitigation modules, helping your employees identify and bridge the gaps in building and deploying AI/ML models at scale and within modest durations.
Future-Agnostic Innovation: A workforce trained in next-gen technologies like AI and ML can innovate new solutions that make your business adaptable to tech advancements, staying competitive in an evolving tech ecosystem.

Make Your Team Proficient

Training Objectives

Learn to design and implement AI/ML models from initial scoping through to deployment
Identify and rectify gaps in developing and scaling these models
Master techniques to evaluate and optimize AI/ML models for specific projects
Improve interdisciplinary team collaboration within AI projects
Learn how to strategize for reliability, scalability, and performance, maximizing ROI in AI/ML initiatives

Core Training Modules

Maths and Programming Pre-requisites
1. Fundamentals of statistical analysis and probability theory
2. Calculus and linear algebra with an emphasis on tensors
3. Basics of Python with a focus on data science
4. TensorFlow for implementing tensor operations
5. Data munging techniques for preprocessing and transforming tabular data
Foundations of Machine Learning and Artificial Intelligence
1. How to approach problems using data and algorithms in AI/ML workflows
2. Data types and models:
3. Gradient-boosted models for structured datasets
4. Convolutional Neural Networks for image processing
5. Recurrent Neural Networks for sequential data analysis
6. Transformers and GPT models for text processing
7. Reinforcement Learning for robotics
8. Deep Learning fundamentals
9. Shallow neural networks - Sigmoidal NNs and Logistic Regression
10. Network training techniques including Momentum, AdaGrad, and ADAM
11. Practical implementation with Keras
Computer Vision
1. Fundamental use cases
2. Convolutional operations including the use of kernels, padding, and feature maps
3. Pooling techniques for reducing spatial dimensions
4. CNNs for image classification
5. Transfer learning to leverage pre-trained models for new tasks
6. Residual connections and batch normalization for deeper neural networks
7. Convolution methods - depthwise separable convolution and the Xception architecture
8. Object localization and detection using YOLO
9. Image segmentation with UNet and DeepLab
10. Best practices and tools
Designing Machine Learning Systems
1. Basics of cloud computing
2. Introduction to AWS and Azure, with a focus on specific services - Cloud9 and EC2 instances
3. AWS SageMaker for deploying ML models
4. Cloud computing tools - AWS ML Tools, Google Cloud, and IBM Watson
Practical MLOps
1. Fundamentals of DevOps, MLOps, Continuous Integration (CI) and Continuous Deployment (CD)
2. Basics of version control with Git and common commands like clone, pull, push, branch, commit & resolving merge conflicts
3. How to implement containerization with Docker and Docker Compose files for managing multi-container setups
4. CI tools and Data Version Control (DVC) with GitHub Actions
5. MLOps tools including Jenkins, DagsHub, Weights, and Biases
6. Basics of Python Flask
Natural Language Processing (NLP)
1. Foundations of NLP
2. Data preprocessing techniques for language models - Text Vectorization Layer, Standardization, Vocabulary Indexing, Embedding Word Vectors, TF-IDF
3. Basics of Tokenization
4. Bag of Words (BoW)
5. Attention mechanism and transformer encoders for comprehension
6. Architecture and applications of BERT
Representation Learning, Generative Models, and Research Trends
1. Fundamentals of Representation Learning
2. Decoder-only GPT class models
3. LLMOps vis-a-vis tools and platforms such as LangChain and the OpenAI API
4. Basics of prompt engineering, RAG (Retriever-Augmented Generation), and LoRA (Low-Rank Adaptation)
5. Generative Adversarial Networks (GANs) and other diffusion models
6. Autoencoders and their use in pre-training models
7. How to implement Reinforcement Learning through Human Feedback (RLHF)
8. Guardrails and processing techniques for LLM outputs
Parallel Computer Architecture and Programming Models
1. Fundamentals of Parallel Programming Paradigms via OpenMP
2. Applications of OpenMP for parallel algorithms - k-means clustering, histogram computation
3. Other parallel programming paradigms such as MPI and CUDA
4. Submitting jobs on computing clusters
5. Comparing Python parallel programming - Multi-Threaded vs Multi-Processor
6. Introduction to PyOMP and PythonMPI
Machine Learning at Scale
1. Utility of GPUs in ML through architecture essentials and the specific roles of tensor cores
2. Distributed computing techniques using TensorFlow
3. Introduction to Parallel ML libraries including Dask, Horovod, Ray, & RAPIDS, and GPU-accelerated libraries such as cuDF, cuML, and cuPY

Frequently Asked Questions- Answered!

All trainings

Data Science

AI & ML Ops Fundamentals Training

Get ahead in AI and ML with our AI and MLOps training from Uptut. Make your teams learn through engaging, hands-on projects and real-world applications. Enroll now!

1500+ users onboarded

Program Overview

Training Objectives

Learn to design and implement AI/ML models from initial scoping through to deployment
Identify and rectify gaps in developing and scaling these models
Master techniques to evaluate and optimize AI/ML models for specific projects
Improve interdisciplinary team collaboration within AI projects
Learn how to strategize for reliability, scalability, and performance, maximizing ROI in AI/ML initiatives

Key training modules

Maths and Programming Pre-requisites
1. Fundamentals of statistical analysis and probability theory
2. Calculus and linear algebra with an emphasis on tensors
3. Basics of Python with a focus on data science
4. TensorFlow for implementing tensor operations
5. Data munging techniques for preprocessing and transforming tabular data
Foundations of Machine Learning and Artificial Intelligence
1. How to approach problems using data and algorithms in AI/ML workflows
2. Data types and models:
3. Gradient-boosted models for structured datasets
4. Convolutional Neural Networks for image processing
5. Recurrent Neural Networks for sequential data analysis
6. Transformers and GPT models for text processing
7. Reinforcement Learning for robotics
8. Deep Learning fundamentals
9. Shallow neural networks - Sigmoidal NNs and Logistic Regression
10. Network training techniques including Momentum, AdaGrad, and ADAM
11. Practical implementation with Keras
Computer Vision
1. Fundamental use cases
2. Convolutional operations including the use of kernels, padding, and feature maps
3. Pooling techniques for reducing spatial dimensions
4. CNNs for image classification
5. Transfer learning to leverage pre-trained models for new tasks
6. Residual connections and batch normalization for deeper neural networks
7. Convolution methods - depthwise separable convolution and the Xception architecture
8. Object localization and detection using YOLO
9. Image segmentation with UNet and DeepLab
10. Best practices and tools
Designing Machine Learning Systems
1. Basics of cloud computing
2. Introduction to AWS and Azure, with a focus on specific services - Cloud9 and EC2 instances
3. AWS SageMaker for deploying ML models
4. Cloud computing tools - AWS ML Tools, Google Cloud, and IBM Watson
Practical MLOps
1. Fundamentals of DevOps, MLOps, Continuous Integration (CI) and Continuous Deployment (CD)
2. Basics of version control with Git and common commands like clone, pull, push, branch, commit & resolving merge conflicts
3. How to implement containerization with Docker and Docker Compose files for managing multi-container setups
4. CI tools and Data Version Control (DVC) with GitHub Actions
5. MLOps tools including Jenkins, DagsHub, Weights, and Biases
6. Basics of Python Flask
Natural Language Processing (NLP)
1. Foundations of NLP
2. Data preprocessing techniques for language models - Text Vectorization Layer, Standardization, Vocabulary Indexing, Embedding Word Vectors, TF-IDF
3. Basics of Tokenization
4. Bag of Words (BoW)
5. Attention mechanism and transformer encoders for comprehension
6. Architecture and applications of BERT
Representation Learning, Generative Models, and Research Trends
1. Fundamentals of Representation Learning
2. Decoder-only GPT class models
3. LLMOps vis-a-vis tools and platforms such as LangChain and the OpenAI API
4. Basics of prompt engineering, RAG (Retriever-Augmented Generation), and LoRA (Low-Rank Adaptation)
5. Generative Adversarial Networks (GANs) and other diffusion models
6. Autoencoders and their use in pre-training models
7. How to implement Reinforcement Learning through Human Feedback (RLHF)
8. Guardrails and processing techniques for LLM outputs
Parallel Computer Architecture and Programming Models
1. Fundamentals of Parallel Programming Paradigms via OpenMP
2. Applications of OpenMP for parallel algorithms - k-means clustering, histogram computation
3. Other parallel programming paradigms such as MPI and CUDA
4. Submitting jobs on computing clusters
5. Comparing Python parallel programming - Multi-Threaded vs Multi-Processor
6. Introduction to PyOMP and PythonMPI
Machine Learning at Scale
1. Utility of GPUs in ML through architecture essentials and the specific roles of tensor cores
2. Distributed computing techniques using TensorFlow
3. Introduction to Parallel ML libraries including Dask, Horovod, Ray, & RAPIDS, and GPU-accelerated libraries such as cuDF, cuML, and cuPY

Build a high-performing, job-ready tech team.

Personalise your team’s upskilling roadmap and design a befitting, hands-on training program with Uptut

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Hands-on Experience with Tools

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Opt-in Certifications

AWS, Scrum.org, DASA & more

100% Live

on-site/online training

Hands-on

Labs and capstone projects

Lifetime Access

to training material and sessions

How Does Personalised Training Work?

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Skill-Gap Assessment

Analysing skill gap and assessing business requirements to craft a unique program

1

Personalisation

Customising curriculum and projects to prepare your team for challenges within your industry

2

Implementation

Supplementing training with consulting support to ensure implementation in real projects

3

Why AI and ML Ops for your business?

Enhanced Operational Efficiency: AI and ML will generate US$4.4 trillion in business value by next year! Upskilling your teams in these areas will help in boosting their productivity and streamline your workflows, significantly boosting overall operational efficiency.
Improved Decision-Making: Training in AI and ML Ops sharpens employees' skills in working with and extracting valuable insights from complex data sets. This not only prepares them to make informed, precise decisions but also empowers them to leverage advanced analytical tools for better business outcomes.
Reduced Operational Risks: This AI and ML Ops training covers risk mitigation modules, helping your employees identify and bridge the gaps in building and deploying AI/ML models at scale and within modest durations.
Future-Agnostic Innovation: A workforce trained in next-gen technologies like AI and ML can innovate new solutions that make your business adaptable to tech advancements, staying competitive in an evolving tech ecosystem.

Who will Benefit from this Training?

AI and Data Science practitioners
IT professionals planning to transition to AI/ML development
Operations professionals planning to upgrade to AI and MLOps

Lead the Digital Landscape with Cutting-Edge Tech and In-House " Techsperts "

Discover the power of digital transformation with train-to-deliver programs from Uptut's experts. Backed by 50,000+ professionals across the world's leading tech innovators.

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Frequently Asked Questions

1. What are the pre-requisites for this training?

The training does not require you to have prior skills or experience. The curriculum covers basics and progresses towards advanced topics.

2. Will my team get any practical experience with this training?

With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.

3. What is your mode of delivery - online or on-site?

We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.

4. Will trainees get certified?

Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.

5. What do we do if we need further support after the training?

We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.

Program Overview

Make Your Team Proficient

Training Objectives

Hands-on Experience with Tools

Core Training Modules

How does personalized training work?

Who will benefit from this training?

Is this training right for your team?

Frequently Asked Questions- Answered!

Accelerate Business Returns with In-Demand Technology and High-Performing Teams

AI & ML Ops Fundamentals Training

Program Overview

Training Objectives

Key training modules

Build a high-performing, job-ready tech team.