In today’s rapidly evolving technological landscape, organizations are increasingly relying on data-driven solutions for a competitive edge. To effectively manage these solutions, three operational practices have emerged: AIOps, MLOps, and LLMOps.
AIOps, or Analytics and AI Operations, focuses on managing analytics and AI solutions throughout their lifecycle. It ensures reliability, scalability, and accurate insights for data-driven decision-making.
MLOps, or Machine Learning Operations, specializes in the lifecycle management of machine learning models. It addresses challenges like versioning, reproducibility, scalability, and monitoring to enable smooth operation and governance.
LLMOps, standing for Large Language Model Operations, concentrates on the deployment, fine-tuning, and management of large-scale language models. Given the vastness and complexity of these models, LLMOps ensure they’re optimized for tasks such as natural language understanding, translation, and generation, among others.
While each practice aims to efficiently manage data-driven systems, they cater to different challenges and requirements. Understanding AIOps, MLOps, and LLMOps allows organizations to implement the right operational frameworks for maximizing the value of their data-driven solutions. In the coming parts of this article, we will delve further into these three processes, explaining what they are, their roles, and their challenges.
What is AIOps?
AIOps, which stands for Analytics and AI Operations, is a practice that focuses on efficiently managing and optimizing analytics and AI solutions within organizations. It encompasses the entire lifecycle of analytics models, from data collection to deployment, monitoring, and ongoing maintenance.
AIOps aims to ensure that analytics solutions are not only reliable and scalable but also provide accurate insights for data-driven decision-making.
Challenges of AIOps
- Analytics Solution Adoption: One of the unique challenges in AIOps is ensuring the adoption and utilization of analytics solutions throughout the organization. Resistance to change, lack of understanding, and cultural barriers can hinder the effective implementation of analytics-driven decision-making. Encouraging stakeholders to embrace analytics and providing proper training and support is crucial to address this challenge.
- Data Privacy: AIOps requires organizations to establish robust data governance practices and comply with relevant regulations, such as data privacy laws and industry standards. Ensuring data security, privacy protection, and compliance with regulatory frameworks can be complex and resource-intensive. Organizations need to invest in policies, procedures, and technologies to address these challenges.
MLOps :
What is MLOps?
MLOps, short for Machine Learning Operations, is a set of practices that focus on managing the lifecycle of machine learning models. It involves streamlining the processes of model development, deployment, monitoring, and maintenance to ensure the efficient and effective operation of ML solutions in real-world applications.
Challenges of MLOps
- Data Management: Handling large volumes of data for training machine learning models can be a complex task. Ensuring data quality, preprocessing, and privacy protection pose challenges, as organizations need to carefully manage and prepare their data to produce reliable and accurate models.
- Model Versioning: Keeping track of different model versions, code changes, and dependencies is crucial for reproducibility and collaboration. Ensuring consistent environments across teams and being able to replicate and reproduce model results can be a challenging puzzle to solve. In such cases, the usage of specialized Machine learning versioning tools such as Weights and Biases can greatly assist in versioning your machine learning models.
- Deployment and Scalability: Deploying machine learning models into production environments is a significant challenge. Seamlessly integrating models with existing systems, ensuring scalability to handle varying workloads, and optimizing resource utilization are key hurdles that require careful planning and execution.
LLMOps :
What is LLMOps?
LLMOps, which stands for Large Language Model Operations, focuses on the practical side of managing and optimizing large language models. It involves a set of practices and methodologies to ensure the smooth development, deployment, and ongoing management of these models in real-life scenarios.
For instance, LLMOps plays a crucial role in fine-tuning and optimizing language models like ChatGPT, enabling them to power chatbots, language translation systems, and platforms that understand and process natural language. By implementing effective LLMOps strategies, organizations can harness the power of large language models to enhance user interactions, improve language-related applications, and deliver more human-like responses.
Challenges of LLMOps
- Data Management and Preprocessing: Similar to normal machine learning models but even on a bigger scale, the process of handling and preprocessing large volumes of textual data required for training language models can be challenging. Organizations need robust strategies for data collection, cleaning, preprocessing, and ensuring data quality to train accurate and reliable models.
- Ethical and Bais Considerations: Large language models can exhibit biases or generate inappropriate content. Addressing ethical considerations, identifying and mitigating biases, and ensuring fairness in language processing are important challenges in LLMOps.
PROGRAM CURRICULUM
PRE-PROGRAM PREPARATORY CONTENT (3 WEEKS)
INTRODUCTION TO PYTHON
Build a foundation for the most in-demand programming language of the 21st century.
PYTHON FOR DATA SCIENCE
Learn how to manipulate datasets in Python using Pandas, which is the most powerful library for data
preparation and analysis.
DATA VISUALISATION IN PYTHON
Humans are visual learners and hence no task related to data is complete without visualisation. Learn
to plot and interpret various graphs in Python and observe how they make data analysis and drawing
insights easier.
DATA ANALYSIS USING SQL (OPTIONAL)
Data in companies is definitely not stored in excel sheets! Learn the fundamentals of database and extract
information from RDBMS using the structured query language.
ADVANCED SQL AND BEST PRACTICES (OPTIONAL)
Apply advanced SQL concepts like windowing and procedures to derive insights from data and answer
pertinent business questions.
DATA ANALYSIS IN EXCEL
Taught by one of the most renowned data scientists in the country (S.Anand, CEO, Gramener), this module
takes you from a beginner level Excel user to an almost professional user.
ANALYTICS PROBLEM SOLVING
This module covers concepts of the CRISP-DM framework for business problem-solving.
MATH FOR MACHINE LEARNING
Learn the prerequisite mathematical tools and techniques for ML – Linear Algebra and Multivariable Calculus.
STATISTICS AND EXPLORATORY DATA ANALYTICS (5 WEEKS)
EXPLORATORY DATA ANALYSIS
Learn how to find and analyse the patterns in the data to draw actionable insights.
CLOUD ESSENTIALS: INTRO TO GIT & GITHUB
Learn version control, collaborating, portfolio making using git. Understand the process of creating repository. Learn
the process of creating github portfolio using github pages with jekyll
INFERENTIAL STATISTICS
Build a strong statistical foundation and learn how to ‘infer’ insights from a huge population using a
small sample.
HYPOTHESIS TESTING
Understand how to formulate and validate hypothesis for a population to solve real-life
business problems.
LENDING CLUB CASE STUDY
Determine which customers are at risk of default and what are their characteristics so as to
avoid providing loans to similar people in the future.
MACHINE LEARNING I (7 WEEKS)
LINEAR REGRESSION
Venture into the machine learning community by learning how one variable can be predicted
using several other variables through a housing dataset where you will predict the prices of
houses based on various factors.
LINEAR REGRESSION ASSIGNMENT
Build a model to understand the factors car prices vary on and help a Chinese company enter
the US car market.
LOGISTIC REGRESSION
Learn your first binary classification technique by determining whether customers of a telecom
operator are likely to churn to help the business retain customers.
NAIVE BAYES
Understand the basic building blocks of Naive Bayes and learn how to build an SMS Spam
Ham Classifier using Naive Bayes technique.
MODEL SELECTION
Learn the pros and cons of simple and complex models and the different methods for quantifying
model complexity, along with regularisation and cross validation.
MACHINE LEARNING II (7 WEEKS)
ADVANCED REGRESSION
Understand generalised regression and different feature selection techniques, along with the
perils of overfitting and how it can be countered using regularisation.
ADVANCED REGRESSION ASSIGNMENT
Build a model to understand the factors house prices vary on and help an American company
enter the Australian housing market.
SUPPORT VECTOR MACHINE (OPTIONAL)
Learn how to find a maximal marginal classifier using SVM, and use them to detect spam emails,
recognise alphabets and more!
TREE MODELS & RANDOM FORESTS
Learn how the human decision making process can be replicated using a decision tree and
other powerful ensemble algorithms.
MODEL SELECTION: PRACTICAL CONSIDERATIONS
Given a business problem, how do you choose the best algorithm? Learn a few practical tips
for doing this here.
BOOSTING
Learn how weak learners can be ‘boosted’ with the help of each other and become strong
learners using different boosting algorithms such as Adaboost, GBM, and XGBoost.
UNSUPERVISED LEARNING: CLUSTERING
Learn how to group elements into different clusters when you don’t have any pre-defined labels
to segregate them through K-means clustering, hierarchical clustering, and more.
UNSUPERVISED LEARNING: PRINCIPAL COMPONENT ANALYSIS
Understand important concepts related to dimensionality reduction, the basic idea and the
learning algorithm of PCA, and its practical applications on supervised and unsupervised
problems.
TELECOM CHURN CASE STUDY
Solve the most crucial business problem for a leading telecom operator in India and southeast
Asia – predicting customer churn.
DEEP LEARNING (8 WEEKS)
INTRODUCTION TO NEURAL NETWORKS
Learn the most sophisticated and cutting-edge technique in machine learning – Artificial Neural
Networks or ANNs.
CONVOLUTIONAL NEURAL NETWORKS – INDUSTRY APPLICATIONS
Learn the basics of CNN and OpenCV and apply it to Computer Vision tasks like detecting
anomalies in chest X-Ray scans, vehicle detection to count and categorise them to help the
government ascertain the width and strength of the road.
CONVOLUTIONAL NEURAL NETWORKS – ASSIGNMENT
Build a neural network from scratch in Tensorflow to identify the type of skin cancer from image
RECURRENT NEURAL NETWORKS
Ever wondered what goes behind machine translation, sentiment analysis, speech recognition etc. ? Learn how RNN helps in these areas having sequential data like text, speech, and
videos, etc.
NEURAL NETWORKS PROJECT: GESTURE RECOGNITION
Make a Smart TV system which can control the TV with user’s hand gestures as the remote control.
NATURAL LANGUAGE PROCESSING (7 WEEKS)
LEXICAL PROCESSING
Do you get annoyed by the constant spams in yor mail box? Wouldn’t it be nice if we had a
program to check your spellings?
In this module learn how to build a spell checker & spam detector using techniques like phonetic hashing,bag-of-words, TF-IDF, etc.
SYNTACTICAL PROCESSING
Learn how to analyse the syntax or the grammatical structure of sentences using POS tagging
and Dependency parsing.
SYNTACTIC PROCESSING – ASSIGNMENT
Use the techniques such as POS tagging and Dependency parsing to extract information from
unstructured text data.
SEMANTIC PROCESSING
Learn the most interesting area in the field of NLP and understand different techniques like
word-embeddings, topic modelling to build an application that extracts opinions about socially
relevant issues.
CASE STUDY: CLASSIFYING CUSTOMER COMPLAINT TICKETS
In this case study you will create a solution that will help in identifying the type of complaint
ticket raised by the customers of a multinational bank.
ELECTIVE 1:
MLOPS (15 WEEKS)
PRE-REQUISITE MODULE
Builds upon foundational knowledge of DevOps, focusing on its application in the context of
machine learning.
INTRODUCTION TO MLOPS
This module provides an overview of MLOps, focusing on the principles and practices of
integrating machine learning into the software development lifecycle.
DESIGNING MACHINE LEARNING SYSTEMS
Guides students in designing ML systems from ideation to prototyping to product delivery,
emphasizing robustness, reusability, reproducibility and maintainability.
EXPERIMENTING WITH DATA AND MODEL USING MLFLOW
Hands-on experience with MLflow, managing end-to-end machine learning lifecycle, including experiment 4
tracking, model packaging, and version management.
AUTOMATING AND ORCHESTRATING PIEPELINES WITH AIRFLOW
Students will learn how to create and schedule workflows, manage dependencies between tasks,
and monitor pipeline execution using Airflow.
BUILDING CONTINUOUS LEARNING INFRASTRUCTURE
This module covers the concepts and techniques required to establish a continuous learning
infrastructure for ML models. Students will learn about data drift detection, model retraining
strategies, and deployment strategies for updated models.
MLOPS PROJECT
In this assignment, students will apply the concepts and tools learned throughout the curriculum
to develop an end-to-end MLOps solution.
ADVANCED NLP – INTRODUCTION TO ATTENTION MECHANISM
This module focuses on building sequence to sequence models using attention mechanism to
build a Neural Machine Translation(NMT) model.
ADVANCED NLP – INTRODUCTION TO TRANSFORMERS
Explores Transformers architecture in NLP, diving deeper into self-attention mechanisms, multihead attention, and positional encoding, with a focus on fine-tuning BERT models for sentence
similarity.
ADVANCED CV – OBJECT DETECTION & SEMANTIC SEGMENTATION
Covers advanced computer vision techniques, including object detection and semantic
segmentation, with hands-on experience in training and evaluating models using popular
algorithms and frameworks.
MLOPS + DEPLOYMENT: DL (THEORY)
Provides theoretical foundations for deploying deep learning models in MLOps pipelines, including model training with AWS SageMaker and deployment considerations such as model serving,
scalability, and performance optimization.
MLOPS + DEPLOYMENT: DL (CASE STUDY)
In this case study, you will apply all your learnings from the previous module to perform an end
to end deployment of a DL model using AWS Sagemaker.
ELECTIVE 2:
GENERATIVE AI (15 WEEKS)
ADVANCED NLP – INTRODUCTION TO ATTENTION MECHANISM
This module focuses on building sequence to sequence models using attention
mechanism to build a Neural Machine Translation(NMT) model
ADVANCED NLP – INTRODUCTION TO TRANSFORMERS
Explores Transformers architecture in NLP, diving deeper into self-attention mechanisms,
multi-head attention, and positional encoding, with a focus on fine-tuning BERT models
for sentence similarity.
INTRODUCTION TO GENERATIVE AI, CHATGPT & PROMPT ENGINEERING
Introduces students to the world of generative AI and various LLMs that have revolutionised the
current industry, and enables learners to dive into that revolution by learning the nitty-gritties
of writing a prompt of generate a desired outputs for complex tasks.
ADVANCED PROMPTING & FINE TUNING IN PYTHON
Dive deeper into prompt engineering and learn how to structure prompts and outputs, and how
you can use advanced prompting techniques such as chain-of-thought prompting, zero- and
few-shot prompting, prompt injunctions, prompt pararmeter tuning. By the end of this module,
learners will become proficient at defining prompts for most complex tasks.
PRODUCT DEVELOPMENT & INTEGRATING SPEECH USING WHISPER API AND
APPLICATION DEPLOYMENT USING FLASK
Learn the fundamentals of product development, and deploy your own GPT-enabled web app
with the use of Flask.
PROMPTING ON MULTIMODAL LLMS LIKE STABLE DIFFUSION, MID JOURNEY
Understand the fundamentals of design, photography and product development to generate
images and multimodal outputs for businesses.
APPLICATIONS OF LLMS IN CODE GENERATION & DATA SCIENCE
Write prompts to generate accurate codes for various general and data tasks, perform basic
data processing and modelling tasks using ChatGPT and Copilot.
GENAI PROJECTS
Apply your learnings to create various GenAI enabled applications such as Interview Gynie,
Pixxel Craft and Shrewd News AI
EMBEDDING LARGE DOCUMENTS WITH LLMS
Understand the concepts of embeddings and take the first step towards building custom LLMs
that involve integrating a database with your GenAI models.
STORING AND INDEXING EMBEDDINGS OF LARGE DOCUMENTS WITH VECTORSTORES
LIKE PINECONE
Embed large documents and datasets with the help of vector stores like Pinecone to enhance
Chat GPT’s ability to understand context, avoid hallucinations, and perform accurately on
data-specific tasks.
INTRODUCTION TO LANGCHAIN AND ITS APPLICATIONS
With the limitations of standalone LLMs, understand how LangChain can be used to overcome
those limitations and help integrate GenAI models on specific data pools.
LANGCHAIN AGENTS, TOOLS, AND VECTORSTORES FOR STORAGE AND RETRIEVAL
Understand how the different components of LangChain such as Models, Prompts, Indexes,
Chains, Memory and Agents help building a GenAI model.
CONNECTING COMPONENTS USING CHAIN AND THE POWER OF TOOLS IN LANGCHAIN
Understand how to connect components using chain, and how different inbuilt tools in LangChain can be leveraged for your models.
SCALE AND DEPLOY GENERATIVE AI APPS USING AZURE OPENAI SERVICES
Deploy your generative AI models using Azure OpenAI services and understand the considerations
that go in when scaling generative AI models.
FUTURE DEVELOPMENTS IN GENERATIVE AI
Understand what the future of AI holds (mitigating risks in AI, RLHF as a product, Multimodal Learning),
both from the architecture and applications perspective.
CAPSTONE (4 WEEKS)
CAPSTONE
Choose from a range of real-world industry woven projects on advanced topics like
Recommendation Systems, Fraud Detection, GANs among many others.
NEWS RECOMMENDER SYSTEM
Build a model to using the concepts of natural language processing and recommender systems
to recommend news stories to users on a popular news platform.
CREDIT CARD FRAUD DETECTION
To build a machine learning model capable of detecting fraudulent transactions. Here you
have to predict fraudulent credit card transactions with the help of machine learning models.
EYE FOR BLIND – (IMAGE CAPTIONING)
Build a model that can help any visually impaired person in understanding image present before them.
It is a deep learning model which can explain the content of an image in the form of speech.
SENTIMENT ANALYSIS BASED PRODUCT RECOMMENDER SYSTEM
Build a sentiment analysis based product recommendation system to recommend the similar products
to the users. Sentiment analysis is used to fine tune the product recommendation system.
SALES FORECASTING
Predict the sales for a european pharma giant using a host of different types of variables. Apply VAR
and VARMAX models to build the appropriate model
STYLE TRANSFER USING GAN’S
Build a Model for converting MRI images from one type (T1) into other (T2) and vice versa.
CycleGAN model is used for producing T2 type MRI images given T1 type input MRI images.
REINFORCEMENT LEARNING (OPTIONAL)
CLASSICAL REINFORCEMENT LEARNING
Ever wondered how Alpha Go beat the best GO player or how Boston Dynamics made robots
that can run. Start your journey with the classical RL algorithms like dynamic programming, Monte Carlo methods, Q Learning to train the state value and action value functions of the policy.
ASSIGNMENT – CLASSICAL REINFORCEMENT LEARNING
Train an agent that’ll beat you in the game of numerical tic-tac-toe everytime you play
DEEP REINFORCEMENT LEARNING
Want to build your own Atari Game? Learn the Q-function or policy using the various Deep Reinforcement Learning algorithms: Deep Q Learning, Policy Gradient Methods, Actor- Critic method.
REINFORCEMENT LEARNING PROJECT
Improve the recommendation of the the rides to the cab drivers by creating a RL based algorithm using
vanilla Deep Q-Learning (DQN) to maximize the driver’s profits and inturn help in retention of the driver
on the cab aggregator service.
RESEARCH METHODOLOGY
WHAT IS RESEARCH?
Familiarise yourself with different aspects of research
• Introduction to research
• Importance of research
• Criticism in research and its importance
• Peer reviews in research and its importance
TYPES OF RESEARCH
Develop an understanding of various research designs and techniques
• Descriptive vs Analytical
• Applied vs Fundamental
• Quantitative vs Qualitative
• Bayesian vs Frequentist Approach
RESEARCH PROCESS
Learn about the different steps in the research process and how to evaluate a literature review
• Research question
• Hypothesis and aims
• Formulating a Problem
• Literature review
RESEARCH PROJECT MANAGEMENT
Learn how to plan the project timelines and arrange for data & software
• Understand the different steps involved in a project cycle
• Project Requirements on Data
• Identifying the milestones in a research project
• Learn how to track the progress using Gantt Chart
REPORT WRITING AND PRESENTATION
Master good scientific writing and proper presentation skills
• Art of writing papers
• Parts of a paper
• Tools to write papers
• Publishing papers: Journals + Seminars
SCIENTIFIC ETHICS
Develop an understanding of the ethical dimension in research
• Citation Methods and Rules
• Honor Code
• Research Claim