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Machine Learning + DevOps (MLOPS) Training by Mr. Vimal Daga

Linux Training

Day 1 - Why we need an OS | Basic commands in linux | Interrupt Signals | Ctrl key shortcuts | Date command and its options | Man command | How to run any command/program in background | Creating the file and directory | Uses of Pipe symbol (222:23)
Summary
Day 2 - Shells in Linux | Local host | Remote host | What is Virtualisation | Bridge adaptor | Ping command | Ifconfig command | Setting up remote host | What is Ssh protocol | What is a process | Ps-aux | Process id | X11 program to launch graphical program | Enabling ssh root login (210:21)
Summary
Day 3 - What is software/package | rpm command | How to check software behind any command | Installing and uninstalling softwares Check softwares in the ISO | What is yum | Yum configuration | How to switch to root user | Vi editor | Vi editor options (154:37)
Summary
4.What is docker | Docker Installation | Different ways of installing OS | Docker host | What is Containerization | What is Container | Docker ps command | Docker pull command | Docker run command | Giving name to a container | Docker start command | Docker attach command (197:19)
Summary
5. script command | running coomand without login on remote system | absolute path | cd .. command | / directory | scp commmand | docker info command | docker rm command | mounting external drive to docker container (202:40)
Summary
6.different type of server | Apache web server | Install the client software | CLI and GUI | HTTP Server | Web server configuraton | Protocols | Web client setup (213:55)
Summary
7. Install podman | custom images | container os | install python3 | docker file | environment | container file | workspace | interactive and non interactive command | docker file keyword | build | docker hub | docker pull | docker push | docker registory | repository | Standzation (175:57)
Summary
8. Install python | docker file | cmd keyword | versioning | build time | run time | history keyword | copy keyword | and keyword | mount code | Entrypoint keyword | docker file keyword (217:32)
Summary
9. sudo concept | root power | sudoer file | sodu command | non- interactive command | password | install httpd | detached | Apache web server | selinux | cgi-bin | backend services (84:33)
Summary
10. Networking | LAN/ NIC/N/W / Ethernet Card | Wireless | IP address | fishing Attack | key network | mac address | octet | bytes | 4 octet | DNS server | look up | IPV4 | binary calculater | layer 7 firewall | server | network program | port number (75:13)
Summary
11. Linux Adminstration V9 Training By Mr. Vimal Daga on 5th May 2024_GMT20240605-053001 (128:41)
Summary 11
12. port number | pating | nating | kubernetes | fault tolerance | availability | container management tool | orchestration tool | kubernetes service | Minikube | kubectl command | cluster info | pod | launch pod | deployment | cri- (79:01)
12.1_Industry Expert Session by ChandraShekhar_GMT20240607-064639_Recording_1920x1080 (119:01)
13. Linux Adminstration V9 Training By Mr. Vimal Daga_GMT20240613-082801 (66:10)
14. Linux Adminstration V9 Training By Mr. Vimal Daga_GMT20240614-085029 (59:31)

Bonus Sessions - Linux

Plain Text | Cipher Text | Private and Public Key | Symmetric and Asymmetric Key | ssh | Key Based Authentication | User Management (217:10)
Useradd Command | adduser Command | GECOS | chfn Command | man Command | finger Command | who Command | w Command | Home Directory | home directory | Bash Shell | Nologin Shell | Shell Program | Interactive Users | Non interactive Users (138:11)
Superuser | General User | System or Service User | Hashing | Epoch Time | Password Aging
User Permissions| Permissions on a file | Permissions on a directory | Modes | chmod Command | chown Command | su Command | Groups | chgrp Command
DAC (Discretionary Access Control) | Linux Permissions | Special Permissions | Sticky Bit | SGID | SUID | Challenge of 'w' Permission | Set sticky bit on a folder | Primary Group | Secondary Group | newgrp command | gpasswd command | Use case of SUID (147:23)
POSIX | ACL | getfacl Command | setfacl Command | Mask | Effective Permissions | Umask | Sudo Power| Admin Level Commands | System Level Commands | /etc/sudoers main configuration file | /etc/sudoers.d secondary configuration file | Wheel Group | Visudo (172:25)
Security Program | Firewall | Network Service | Network Traffic | Firewalld Service | Pre-created rules (Zones) | Custom rules | firewall-cmd Command | Network Interface Card (NIC) | Implement Firewalld on NIC Target | Zones | PAT | Masquerade | Port Forwarding | Rich Rules (194:50)
Basic concepts of Partition | Hard Disk | Different types of Hard Disk | Steps to create Virtual Hard Disk (100:24)
Create a Partition (100:08)
Partition | Format | Mount (94:25)
LVM | Extend the LV (103:24)
LVM | Reduce LV (79:12)

Docker Basic Commands | Launching container | Port Number | Configure Container as Webserver and Database Server | Environmental Variables

Docker Basic Commands | Launching container | Port Number | Configure Container as Webserver and Database Server | Environmental Variables (114:19)
Word Press | MySQL | Set-up Three Tier Architecture | Patting | Hosting a Webpage | Container Linking (122:16)
Docker Networking | NAT | Networking Basics | SDN | Network Infrastructure | Bridge (129:28)
Runtime | Plugins | NAT | Bridge Network Interface | SDN | DHCP | DNS | Subnet | Gateway | IPAM | Custom Network Infrastructure (135:17)

Docker Bonus Session

Docker Basic Commands | Launching container | Port Number | Configure Container as Webserver and Database Server | Environmental Variables (114:19)
Word Press | MySQL | Set-up Three Tier Architecture | Patting | Hosting a Webpage | Container Linking (122:16)
Docker Networking | NAT | Networking Basics | SDN | Network Infrastructure | Bridge (129:28)
Runtime | Plugins | NAT | Bridge Network Interface | SDN | DHCP | DNS | Subnet | Gateway | IPAM | Custom Network Infrastructure (135:17)
PID | Why Docker is Superfast? | Need of OS | Process | Nested Process | Pgrep Command | /proc directory | Bash Shell | Kernel | Cgroup (98:19)

Python Training

Day 1 - Why we need a programming language | Print() function |variables | System() function | Storing multiple data| Data structures | Tuple data structure | List data structure | Len() function | Difference between list and tuple | Slicing operator| Numpy library| Array data structure| Pip command (156:01)
Summary
Day 2 - Dictionary | Pandas | Dataframe | iloc function | identation | if-else | input function | creating menu like program to select from multiple options | Os module (143:54)
Summary
Day 3 - creating Function | Right use case of functions | parameterized function | positional arguments | readability of the code | os module | not operator | for loop | and operator | subprocess.getouput() function (141:12)
Summary
4. Lis | Tuple | Functions | variables | Array | one-dimensional array | two-dimensional array (matrix) | OpenCV library | 3d array (144:23)
Summary
5. Brainstorming (79:30)
6. Boolean | data type | Function | Variable | Keyword | Special keyword | Operator | For loop | String | While loop | Library (120:31)
Summary
7. Gateway interface | CGI program | API | Http protocol | Rest API | Aws cloud | Ec2 service | firewall | launch Ec2 instance | public IP | create web application | web server | install httpd | URL | which command | program file | Executable command | http header | http body | content type (150:54)
Summary
8. Cgi-bin | Web server | input function | search engine | q variable | query string | import cgi | field storage function | security group | ssh login | cell injection | pre tag | html form | action tag | test Api | postman tool | install postman | crud operation | get method | put method (159:32)
Summary
8.1_Python Programming Training By Mr. Vimal DagaGMT20240528-103753 (112:18)
9. Custom data structure | Template| list | Initiatioztion | reference | Multi-threding | parallel | inheritance | single inheritance | base class | drive class | Multiple inheritance | constructor | crud operation | public variable | private keyword | access modifier concept (277:45)
Summary
10. Python Programming Training By Mr. Vimal Daga_GMT20240530-104732 (212:48)
Summary 10.
11. cvzone | Tzdata | Cv2 | hand tracking | hand Detector | Find hand | hand landmarks | import os | if else program | pose tracking (215:47)
Summary 11
12. Class | Object | Memory | RAM | Variable | Function (149:02)
Summary
13. Variable | Function | Object | Class | Keyword | Get method | Set method | Custom data structure | List | Constructor (72:36)
Summary
14. MongoDB | Storage devices | database management system | SQL language | unstructured data | Document oriented data base | row oriented data base | graph databases | Crud operation | CLI & GUI & python | mongo shell | compass | protocol | connections string | data format | get collection (142:03)
Summary
15. pymongo | dir function | mongo client | database | drop database | get database | create database | collection | find method | insert one | cursor | find all | json format | for loop | file handling | operators | $gt | $gte | while loop | constructor | multi tier architecture (128:17)
Summary
16. Python Programming Training By Mr. Vimal Daga_GMT20240613-114805 (109:43)
17. Python Programming Training By Mr. Vimal Daga_GMT20240618-114205 (49:39)

Bonus Session - Python

Lists | text-to-speech | pyttsx3 library | speaker driver | converting strings to audio | DataFrame | resolving row-wise operation limitations faced in dictionaries | Pandas library | loc[] function | .csv files | read_csv() function | describe() function (101:20)
Summary - Lists | text-to-speech | pyttsx3 library
list in Python | memory address | Copy module | shallow and deep copy | deepcopy() function | 2-D & 3-D lists | Functions | Iteration | for loop (105:39)
Summary - list in Python | memory address
Menu program | integration of Python and Linux | If-else conditions | while loops| system| or OS commands in Python | tput setaf <0-7> command | OS module | triple quotes (“””) | input() function | (92:04)
keyword |all types of containers: lists, dicts, sets, strings | File handling | manipulate file | two categories, text file, and binary file | handle file exceptions | append mode| Reading a file | read() | readline() | function tell ()| Closing a file | (125:34)
Summary - keyword |all types of containers: lists, dicts, sets, strings
File handling | read mode ("r"), | write mode ("w") | append mode ("a") | open() function | tell() method | file position: | Seek() function | (92:15)
Summary - File handling | read mode ("r"),
Object Oriented Programming | Classes |data structure. | object | instantiation | data member |member functions | Access Modifiers | Public | Private | Setter and Getter | (90:45)
Summary - Object Oriented Programming | Classes |data structure
Open() function | hard disk is located | “With” keyword | “speech_recognition” | “Pyaudio”| text.lower() | Google Recognize API | (117:59)
Module | function (def) | call function | OS module | if __name__ | "__main__" | infinite loop | (88:35)
Anonymous function |lambda keyword.| adding two numbers function | modulo operator | modulo operator| filter() function | built-in function | database | (90:28)
Server | Socket programming. | Networking program | IP + port Number means | Bind function | netstat -tnip command. | client | (85:45)
Summary - Server | Socket programming. | Networking program
Server side code | client side program | network programming | conn variable | conn.recv | os.system | (70:00)
Summary - Server side code | client side program
Server | Socket programming | handle input and output | external sources | IP and port number|while" loop (97:18)
Summary - Server | Socket programming | handle input and output
local IP address | Network programming | Variable|string data | Client and server| while loop (67:16)
Summary - local IP address | Network programming
Threading | Multi-threading | RAM | CPU | Stack memory | function (72:08)
Exception handling | File handling | close() | read() | write() | (88:37)
iteration | TTS | pyttsx3 | program file | speaker | (134:36)

Machine Learning Training

Day 1 -What is machine learning | machine learning works | How to think and find the formula | Artificial General Intelligence | What are Features | What is Feature Selection | Feature elimination | Dependent Variable | Independent Variable | Co-efficient | Hit and trial method | Loss function (107:23)
Summary
Day 2 -What is a dataset | Linear regression | Scikit learn library | What are Features | Dependent and independent variables | Pandas Library | Why to use pandas | Model fitting | Converting 1D data into 2D data | What is Supervised learning | Joblib library (120:51)
Summary
3. Activity (173:33)
4. Accuracy | linear regression | pandas | convert 1D to 2D | coefficient | bias concept | visualization technique | data point | historical data | slope | best fit line | simple linear regression | EDA technique | Matplotlib | scatter function | plot() function | intercept | parameter | residuals (114:59)
Summary
5. Error | Loss function | cost function | Mean Absolute Error (MAE) | Learning Curve | Slope | weightage | bias | mean_absolute_error function | model selection (110:58)
Summary
6. Multi-Linear Regression | Statistical Analysis | Features | weight | Categorical Variable | Handling Categorical Variables | One Hot Encoding | Dummy Variable | Dummy Variable trap (104:09)
Summary
7. dummy variable trap | independent variables | Categorical | test set | training Set | Data Preprocessing | train test split | random state (57:22)
Summary
8. supervised learning | CLassification | Regression | Binary Classification | Sigmoid Function | Logestic Regression | Titanic Data set | Seaborn Library | EDA (138:45)
Summary
9. confusion matrix | split dataset | train test split | random state | true positive | true negative | false positive | false negative | actual value | predicted value | type 1 and type 2 error (111:50)
Summary
10. confusion matrix | split dataset | train test split | random state | true positive | true negative | false positive | false negative | actual value | predicted value | type 1 and type 2 error (120:48)
Summary
11. What is deep learning | Pattern | What is neuron | Input layer | Hidden layer | Output layer | Activation function | Feedforward neural network (121:19)
Summary
12. Tensorflow library | Keras library | Neuron | Neural networks | Artificial neural network | Activation function | Perceptron | Creating own neural network | Dense function | Data imputation | Loss function (91:48)
Summary
13. Churm modelling dataset | Brain | Nodes | Training model for churn modelling | Supervised learning | Sequential model | Relu activation function | Sigmoid activation | function | Input layer | Hidden layer (114:10)
Summary
14. Creating model for churn modelling | Feature engineering on churn dataset | Multi-layer perceptron | Kernel initialiser | Glorot uniform initialiser (101:44)
Summary
15. Wines dataset | Creating model for wines dataset | One-hot encoding | Multicollinearity | Softmax activation function | Output attribute | Input attribute (80:38)
Summary
16. Training on wines dataset | HeNormal initialisation | Input_dim | Softmax activation function | Categorical crossentropy loss | Adam optimizer | Matrics | Epochs (90:56)
Summary
17. Machine Learning Training By Mr. Vimal Daga (93:11)
Summary 17
18. Auto feature extraction | convolution | pixel | records | stride | convore layer | feature map | convolution2D | Sequential | kernel size | brain lobes | occipital lobes | activation function | input data | input shape | 3D image | pooling | mean pooling | max pooling | average pooling (109:33)
Summary

Bonus Session - MLOps Training

Mlops Introduction (152:53)
python basics | List and nested list in python | Data analysis and pandas (267:16)
Numpy Array | Image processing | Image cropping | Data analysis basiccs and linere regression (275:45)
Regression model concept | Linear regression | Image processing (247:13)

Prompt Engineering Training

1. prompt engineering | What is Generative AI | Contexts | Long term memory | What is LSTM | Context keyword | models | What is LLM | OpenAI | Hallucination problem | What is fine tuning | OpenAI playground | Some basic prompts example | Giving roles to the chatgpt | What is system role (281:01)
Summary 1
2. User role | System role | Assistant role | LLM module | API Call | ChatGPT | openai play ground | Temperature | parameters | openai library | chat competition model | context window | GPT-3. 5 turbo | Tokens | Token Id | Embedding model | open ai Tokenizer | pricing | Tokens | Maximum Tokens (184:19)
Summary 2
3. Generative | zero shot problem | QNA type prompting | few shot prompting | Chain of thought | fine tuning | think step by step | input keyword | contact (238:59)
Summary 3

Deep Learning Sessions

Session 1-Artificial Neural Networks (ANN)|machine learning model|human brain and neurons|accuracy |feature selection|ANN function|layers | (213:38)
Session 2-dataset|churn modelling|accuracy|chat-gpt|perception|multi-layer|feedforward neural network| (153:12)
Session 3-multi class classification in deep learning|sigmoid|wine dataset (107:07)
Session 4-computer vision in deep learning|cvzone|module|face detection module|hand tracking module (138:54)
Session 5-create code for face detection|multi-factor authentication|face recognition| (71:20)
Session 6-record|Introduction to Convolutional Neural Networks (CNN)| (75:22)
Session 7-create code for Convolutional Neural Networks (CNN) (96:38)
Session 8-pooling layer|convolution layer|flatten|dense|sequential| (113:22)
Session 9-Introduction of recurrent neural network (RNN) (120:52)
Session 10-data preprocessing|one dimensionalneural network|time series|google stock price test data set (93:12)
Session 11-create code for google stock price test data set (67:15)

NLP Training

Session 1 - 4th Sept-Difference between NLP and nlu | sentiment analysis | implementation of textblob | polarity | subjectivity | objectivity (218:30)
Session 2 - 5th Sept-Implementation on dataset | data handling with pandas | textblob implementation on data (165:28)
Session 3 - 6th Sept-Tokenization | lemmatization | stemming | implementation of nltk (310:18)
Session 4 - 7th Sept-nltk installation | WordNet | Spacy | Automatic Conversion from uppercase to lowercase (113:47)
Session 5 - 11th Sept-Revision Session (66:16)
Session 6 - 12th Sept-NLU |introduction of spacy |token ID|read token|mapping| (101:52)

Certified Kubernetes Administrator & Certified Kubernetes Application Developer

DEVOPS Tools - Mastering Jenkins Session By Mr. Vimal Daga Feb 24

1.Why Jenkins ? | Jenkins Alternative Tool | Testing automation tool | SCM -GitHub | DevOps in Industry | CI/CD | Pull & Push Code | End To End Process | Reprovisioning | Java In Jenkins | Automation tools | Private & Public Key Concept | DevOps methodology | Jenkins Installation (127:21)
Summary - Session 1
1.1 Revision session (59:29)
2. " Why do we Need Jenkins? | Manual Setup & Automated Setup Concept | Jobs/Item/Projects In Jenkins | Sudo Power to Jenkins | Privileges Concepts | Interactive & Non-Interactive Commands | Web-Server Configuration Via Jenkins | Permission Concepts In Linux | SCM-GitHub | GitHub Repository (181:42)
Summary Session-2
3. Importance Of Plugins | Multi-System Concept | Distributed Scheduling Concept | Jobs(Executors) | Credentials In Jnekins | Master-Slave Setup | Pre-requisites For Slave-Configuration | Slave Agent Program | Workspace/Root-Directory | Launch Container Via Jenkins | SCM-Github (216:15)
Summary - Session 3
4. Working Difference of Master & Slave Node | Uses Of Triggers | Deployment Strategies | End To End Automation Concept | Integration of Jenkins with K8S & GitHub | Code push to GitHub via GitBash | Poll SCM | GitHub-Webhook | Image push To docker hub | Test Cases-Manual & Automated (197:03)
Summary - Session 4
5. Slave nodes | Jobs | Parameter | Function | String parameter | Default value | Job ID | Environment veriable | Build number variable | Job name variable | SCM tools | Build the Docker image | Docker file | Docker hub | Overwrite image | Rollback | Trigger | Multiple stages | Post build actions (153:54)
Summary - Session 5
5.1 Revision Session (67:53)
6. Pipeline As A code | Permanent Agent(Slave) | Dynamic Provisioning | Docker Plugin | Cloud | Daemon | Security Groups | Docker Image | Docker Hub | Slave Node Via SSH | Slave Node Configuration | JDK Setup In Node | Image Pull From DockerHub | Jenkins Docker Agent | Pull Strategy Of Agent | (165:30)
Summary - Session 6
7. " Automations | Dependency | Job creation | WebUI, CLI and API | Job/Pipeline | Create job Automatically | Create a pipeline Automatically | Pipeline as code (PAC) | Launch Jenkins in kubernetes | Expose Jenkins | Jenkins pod | Logs command | Pipeline plugin | SCM tools (183:28)
Summary - Session 7
Jenkins Session (112:18)

Jenkins Extra Sessions

What is Devops ? (27:38)
Continuous deployment | Continuous testing | Continuous Integration (74:49)
Maven integration with Jenkins (79:20)
Windows as a Slave node (36:07)
Jenkins Integration with Kubernetes (128:36)
Different ways to interact with Jenkins | Environmental variable| IAM & RBAC in Jenkins| Environmental variable (88:56)

AWS Training

Session 1 - 27th July-cloud computing|EC2, S3, and Lamda|data centers| availability zonesoperating system| launch an instance on AWS (310:06)
Session 2 - 28th July-Python shell|Lambda functions|serverless services|SNS|FaaS (205:20)
Session 3 - 29th July-WebUI, CLI, and API| IAM (Identity and Access Management).|Create user|AdministrationAccess (215:10)
Session 4 - 30th July-create lambda function|staas|storage device/block|S3 (205:34)
Session 5 - 31st July-integrate S3,SNS,and lambda|SNS service (209:24)

Git and GitHub

Session 1 - Introduction and Working of Git (255:55)
Session 2 - Branch, Merge in Git & Connecting to Github (271:38)
Session 3 - Features of Git with GitViz (267:53)
Session 4 - Solving multiple use case using with the help of GitViz (274:38)
Session 5- Git And Github QnA | Revision (110:42)

GitLab Sessions

Session 1 - GitLab Introduction | CI-Pipeline | Pipeline As A Code | Jobs | GitLab Runner | SCM (239:00)
GitLab- Session 1 - Summary
Session 2 - SDLC | Shared Runner | Dedicated Runner | AWS Instance As Runner | Pipeline As Code (156:51)
GitLab- Session 2 - Summary
Session 3 - Setting up CI/CD pipeline on two runners (215:27)
GitLab- Session 3 - Summary
Session 4 - Advance pipeline concepts and Variables (252:17)
GitLab- Session 4 - Summary
Session 5 - 20th August - Integration of GitLab and Kubernetes | Set-up Infrastructure | GitOps | Xac | Runner | Kubernetes Cluster | Pre-created Terraform code | Environment | Job Template | (300:11)
GitLab- Session 5 - Summary

Drive Link

GitLab Drive Link

Mlops Training

Mlops Introduction (152:53)
python basics | List and nested list in python | Data analysis and pandas (267:16)
Numpy Array | Image processing | Image cropping | Data analysis basiccs and linere regression (275:45)
Regression model concept | Linear regression | Image processing (247:13)
Linear regression | Python basics | Image processing (250:49)
Linear regression , Prediction, Ml model, Matplotlib, Sklearn, Seaborn, Loss function, Metrices, Feature elimination (265:37)
Linear regression | Feature selection | multi linear regression | computer vision (232:48)
29. Logistic regression | Lazy learning | K-nearest neighbours algo | Pattern recognizing KNN (115:42)
30. Supervised learning | Unsupervised learning | Clustering | Soft clustering ( soft kmeans ) (139:24)
31. Feature scaling | Standardization | Normalization | Kmeans | Iterations (136:03)
Computer vision ( opencv2 ) | Pre-trained model | Haarcascade model | Ip webcam integration | Multi linear regression (263:24)
Multi linear regression | Feature selection | Feature engineering concept | Categorial variable | Dummy variable | Multi co-linearity, (233:42)
Dimensity reduction | feature selection | feature extraction | linear regression | p-value and significance level | OLS and backward elimination and wrapper methond (163:54)
neural learning / Dl | image processsing | OLS | feature selection | gretl | graphs | object detection | face recognization | docker | linux basics (271:08)
Tensorflow | Scalar | lazy execution | Eager execution | Perceptron model | Gradient descent (200:01)
Graph | Tensorflow | DL | Neural Network | Artificial neural network | Activation function | CNN concept (145:55)
Activation function | DL | Regression | Classification | Feed forward Forward Propogation | Back propagation | Keras (267:57)
Neural network | Data visualization | graph | folium | Object detection | CNN | YOLO-Setup and Use | Optimizer | Initializer (257:09)
Data visualization | Classification | Histogram | Graph | Heatmap | Regression | Binary classification (265:34)
Binary Classification | Model using sigmoid function | Logistic regression | visuals, searborn | graph | bar graph | Discrete variable | frequency distribution table | One hot encoding | Feature Engineering | Missing value resolving using feature engineering | Imputation (123:46)
Visualization | Exploratory data analysis | One hot label encoding | Nan | Binary class | logistic regression | confusion matrix (118:54)
Graph | Seaborn | folium | Seaborn | Pandas | Plotly | Cuffling (115:47)

LLMOps Training specialized in DevOps integrated with GenAI

Session 1 - Generative AI | LLM Model | Few shot Prompt |Chain of thought concept |AI Agents |Frame Work |LangChain |OpenAi Play Ground |Lang chain Tools|Provider|Python |Install Lang chain |Create LLm model |Import Agent |Import Tool|Google Search Library |Google API |SerpApi (110:29)
Summary - Session 1
Session 2 - AI Agent |Langchain Framework |Tools|SerpAPI|Import Google search |create Env file |Create SerpApi Key|Model + Agent + Tools = Chain|Verbose|Create a Chain of Google search (67:37)
Summary - Session 2
Session 3 - Use case of CLI|Agent |Tool|Bash shell|Launch Ec2 instance|install pip|install lang chain model |Create a Python program |Shell tool |Create a Llm model for the CLI |Install AWS commands |Launch Ec2 instance with LLm model (100:13)
Session - Session 3
4. LLMOps - Langchain Training By Mr. Vimal Daga on 16th October_GMT20241016-133629 (112:52)

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