Udemy MLOps Bootcamp Mastering AI Operations for Success AIOps

[0dayddl]

​

Download Free Download : Udemy MLOps Bootcamp Mastering AI Operations for Success AIOps
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:15.97 GB
​

Files Included :

Spoiler

1 What and Why MLOps.mp4 (82.88 MB)
MP4
2 The Stages of MLOps.mp4 (136.31 MB)
MP4
1 Agenda of this section.mp4 (11.28 MB)
MP4
2 Linux Features & Bash.mp4 (205.15 MB)
MP4
3 How to Launch EC2 Instances (Quick Refresh).mp4 (52.34 MB)
MP4
4 Basic Linux Commands of Linux.mp4 (607.07 MB)
MP4
1 Introduction to Jenkins.mp4 (125.52 MB)
MP4
10 Installation of Docker in EC2 Instance.mp4 (42.23 MB)
MP4
11 Configure Github Repo - Webhook - Jenkins Credentials.mp4 (139.26 MB)
MP4
12 Introduction to Jenkins FreeStyle Projects and Pipeline Jobs.mp4 (38.29 MB)
MP4
13 Exploration of Jenkins UI.mp4 (18.52 MB)
MP4
14 Create your first First Jenkins Project.mp4 (35.52 MB)
MP4
15 Test Github Webhook with Jenkins.mp4 (105.66 MB)
MP4
16 Installation of Docker Plugin & System Readiness.mp4 (68.28 MB)
MP4
17 Setup Email Notification with Gmail.mp4 (104.77 MB)
MP4
18 Introduction to CI CT CD Pipeline.mp4 (13.59 MB)
MP4
19 Create CI CT CD Pipeline - Github Dockerhub.mp4 (135.47 MB)
MP4
2 How do we Use Jenkins in MLOps.mp4 (19.14 MB)
MP4
20 Create CI CT CD Pipeline - Training.mp4 (57.06 MB)
MP4
21 Create CI CT CD Pipeline - Testing.mp4 (44.78 MB)
MP4
22 Create CI CT CD Pipeline - Deployment.mp4 (45.3 MB)
MP4
23 Perform Test of Pipeline.mp4 (28.23 MB)
MP4
24 Summary.mp4 (41.35 MB)
MP4
3 Prepare and Package ML Model.mp4 (54.23 MB)
MP4
4 Deploy as API with FASTAPI.mp4 (147.76 MB)
MP4
5 Test FastAPI App.mp4 (99.17 MB)
MP4
6 Create Dockerfile.mp4 (44.57 MB)
MP4
7 Exposing the Application Port as per Dockerfile.mp4 (14.73 MB)
MP4
8 Test Locally using Docker Containers.mp4 (103.83 MB)
MP4
9 Installation of Jenkins on AWS EC2 Instances.mp4 (98.69 MB)
MP4
1 Why Monitoring Machine Learning Models is Important.mp4 (50.54 MB)
MP4
2 What is Monitoring of ML models & When to Update Model in Production.mp4 (22.79 MB)
MP4
3 Why Monitoring Machine Learning Models is Difficult.mp4 (92.2 MB)
MP4
4 Challenge - Who Owns what.mp4 (36.78 MB)
MP4
5 Functional Level Monitoring.mp4 (102.28 MB)
MP4
6 Model Drift.mp4 (64.18 MB)
MP4
7 Operational Level Monitoring.mp4 (24.35 MB)
MP4
8 Tools and Best Practices of Machine Learning Model Monitoring.mp4 (19.45 MB)
MP4
1 Introduction to Continuous Monitoring.mp4 (63.9 MB)
MP4
10 Exploring the Basic Querying Prometheus.mp4 (65.9 MB)
MP4
11 Monitor the Infrastructure with Prometheus.mp4 (29.63 MB)
MP4
12 Monitor the Linux Server with Node Exporter.mp4 (92.67 MB)
MP4
13 Monitor the Client Application using Prometheus.mp4 (28.56 MB)
MP4
14 Monitor the FastAPI Application using Prometheus.mp4 (106.88 MB)
MP4
15 Monitor All EndPoints using Prometheus.mp4 (58.93 MB)
MP4
16 Create Visualization with Grafana.mp4 (123.74 MB)
MP4
17 Trigger Alerts with Grafana.mp4 (105.24 MB)
MP4
2 Use case on Continuous Monitoring.mp4 (27.08 MB)
MP4
3 Introduction to Prometheus.mp4 (29.65 MB)
MP4
4 Architecture of Prometheus.mp4 (88.98 MB)
MP4
5 Metric Types of Prometheus.mp4 (29.89 MB)
MP4
6 Installation of Prometheus.mp4 (117.41 MB)
MP4
7 Introduction Grafana.mp4 (12.56 MB)
MP4
8 Installation of Grafana.mp4 (44.36 MB)
MP4
9 Prometheus Configuration file.mp4 (75.68 MB)
MP4
1 Introduction to Docker Compose.mp4 (27.08 MB)
MP4
2 Hands On - Docker Compose with Flask Application.mp4 (157.13 MB)
MP4
3 Hands On - Docker Compose Prometheus Grafana.mp4 (139.07 MB)
MP4
1 Architecture of ML Application Monitoring.mp4 (43.96 MB)
MP4
2 Hands On Monitoring of ML Application using Prometheus.mp4 (130.12 MB)
MP4
1 Introduction to ML Monitoring.mp4 (63.17 MB)
MP4
2 Setting Up WhyLabs.mp4 (13.75 MB)
MP4
3 Whylogs - Drift Detection, Input, Output, Bias Monitoring.mp4 (398.1 MB)
MP4
4 WhyLogs - Constraints and Drift Reports.mp4 (96.61 MB)
MP4
5 Summary.mp4 (14.9 MB)
MP4
1 Post-Productionalizing ML Models - What Next.mp4 (28.26 MB)
MP4
2 Model Security.mp4 (13.05 MB)
MP4
3 Adversarial Attack.mp4 (16.95 MB)
MP4
4 Data Poisoning Attack.mp4 (4.42 MB)
MP4
5 Distributed Denial of Service Attack (DDOS).mp4 (4.08 MB)
MP4
6 Data Privacy Attack.mp4 (9.48 MB)
MP4
7 How to Mitigate Risk of Model Attacks.mp4 (22.42 MB)
MP4
8 AB Testing.mp4 (37.38 MB)
MP4
9 Future of MLOps.mp4 (18.31 MB)
MP4
1 What do we cover in this section.mp4 (11.62 MB)
MP4
10 Creation of S3 Bucket from Console.mp4 (59.79 MB)
MP4
11 Creation of S3 Bucket from CLI.mp4 (38.36 MB)
MP4
12 Version Enablement in S3.mp4 (45.27 MB)
MP4
13 Introduction EC2 instances.mp4 (43.98 MB)
MP4
14 Launch EC2 instance & SSH into EC2 Instances.mp4 (63.24 MB)
MP4
15 Clean Up Activity.mp4 (7.24 MB)
MP4
2 Create AWS Account.mp4 (46.52 MB)
MP4
3 Setting up MFA on Root Account.mp4 (59.01 MB)
MP4
4 Create IAM Account and Account Alias.mp4 (57.25 MB)
MP4
5 Setup CLI with Credentials.mp4 (40.59 MB)
MP4
6 IAM Policy.mp4 (19.33 MB)
MP4
7 IAM Policy generator & attachment.mp4 (59.02 MB)
MP4
8 Delete the IAM User.mp4 (8.87 MB)
MP4
9 S3 Bucket and Storage Classes.mp4 (128.72 MB)
MP4
1 Introduction to Numpy Library.mp4 (47.95 MB)
MP4
10 Linear Algebra Functions in Numpy.mp4 (16.59 MB)
MP4
11 Shape Modification of Arrays.mp4 (50.99 MB)
MP4
12 np arange().mp4 (17.57 MB)
MP4
13 Relational Operators & Aggregation Functions on Numpy Arrays.mp4 (35.8 MB)
MP4
14 Boolean Masking.mp4 (9.16 MB)
MP4
15 Broadcasting on Numpy Arrays.mp4 (120.64 MB)
MP4
16 Summary of Numpy Library Journey.mp4 (18.9 MB)
MP4
17 Introduction to Pandas.mp4 (18.13 MB)
MP4
18 Working with Pandas Series.mp4 (55.53 MB)
MP4
19 Mathematical Operation on Pandas Series.mp4 (14.09 MB)
MP4
2 Basics of numpy array object.mp4 (15.11 MB)
MP4
20 Dataframes in Pandas.mp4 (85.35 MB)
MP4
21 Working with Data in Pandas DataFrame.mp4 (64.24 MB)
MP4
22 Combining the DataFrames.mp4 (76.98 MB)
MP4
23 Other Functions on Pandas DataFrame.mp4 (66.74 MB)
MP4
24 Advanced Functions in Pandas DataFrame.mp4 (136.31 MB)
MP4
25 Introduction to EDA.mp4 (16.23 MB)
MP4
26 Accessing Google Colab.mp4 (23.9 MB)
MP4
27 Loading the Large Dataset for Working.mp4 (48.36 MB)
MP4
28 Preliminary Analysis on DataFrame.mp4 (76.2 MB)
MP4
29 Null values in the Dataframe.mp4 (53.22 MB)
MP4
3 Import Numpy & Access help.mp4 (20.98 MB)
MP4
30 Data Cleaning.mp4 (67.55 MB)
MP4
31 Introduction to Data Visualization.mp4 (62.17 MB)
MP4
32 Matplotlib Basics.mp4 (51 MB)
MP4
33 Types of Plot - Line plot.mp4 (13.45 MB)
MP4
34 Line Plots Hands On.mp4 (48.39 MB)
MP4
35 Adjusting the Plots.mp4 (50.11 MB)
MP4
36 Plot Adjustment Hands On.mp4 (42.54 MB)
MP4
37 Scatter Plot.mp4 (18.4 MB)
MP4
38 Scatter Plot hands on.mp4 (56.78 MB)
MP4
39 Historgram Plot.mp4 (29.51 MB)
MP4
4 Creation of Array Object - np array().mp4 (22.35 MB)
MP4
40 Introduction to Seaborn.mp4 (17.55 MB)
MP4
41 Exploring the data.mp4 (69.93 MB)
MP4
42 Univariate & Bivariate Plots - Continuous Data.mp4 (74.13 MB)
MP4
43 Plot - Categorical Data.mp4 (50.39 MB)
MP4
44 Advanced Plots in Seaborn.mp4 (39.58 MB)
MP4
45 Which Plot to use.mp4 (27.6 MB)
MP4
5 Attributes of Numpy Array.mp4 (15.08 MB)
MP4
6 Array Indexing and Slicing.mp4 (45.48 MB)
MP4
7 Array Creation Functions.mp4 (59.27 MB)
MP4
8 Copy Arrays.mp4 (21.16 MB)
MP4
9 Mathematical Operation on Numpy Arrays.mp4 (19.22 MB)
MP4
1 About the Section.mp4 (14.42 MB)
MP4
10 Operators in Python Programming Language.mp4 (56.69 MB)
MP4
11 Collection - Strings.mp4 (76.84 MB)
MP4
12 Python String - Builtin Functions - Hands On.mp4 (28.93 MB)
MP4
13 Data Structures - List.mp4 (47.96 MB)
MP4
14 Data Structures - Tuples.mp4 (28.68 MB)
MP4
15 Data Structures - Dictionary.mp4 (28 MB)
MP4
16 Data Structures - Sets.mp4 (26.91 MB)
MP4
17 Explicit and Implicit Casting in Python Programming.mp4 (23.93 MB)
MP4
18 Reading the Input from Keyboard.mp4 (21.33 MB)
MP4
19 String Formatting.mp4 (28.15 MB)
MP4
20 Control Statements - Conditional Statements in Python.mp4 (36.28 MB)
MP4
21 Control Statements - Looping Statements.mp4 (67.02 MB)
MP4
22 List comprehension.mp4 (25.74 MB)
MP4
23 Functions.mp4 (54.74 MB)
MP4
24 Modules in Python.mp4 (26.94 MB)
MP4
25 Classes in Python.mp4 (57.71 MB)
MP4
26 File Handling in Python.mp4 (49.54 MB)
MP4
27 Working with Python Scripts.mp4 (13.53 MB)
MP4
28 Libraries in Python.mp4 (17.05 MB)
MP4
3 Introduction to Python Programming.mp4 (35.55 MB)
MP4
4 Install Anaconda.mp4 (13.87 MB)
MP4
5 Hello World - Python.mp4 (32.45 MB)
MP4
6 Jupyter Lab Quick Tour.mp4 (38.91 MB)
MP4
7 Variables in Python.mp4 (15.37 MB)
MP4
8 Variables - Comments - Markdown Cells - Hands On.mp4 (53.37 MB)
MP4
9 Python Literals - Hands On.mp4 (60.76 MB)
MP4
1 MLOps with MLFlow in 1 Hour.mp4 (427.52 MB)
MP4
2 Kubernetes 101 Part 1.mp4 (208.21 MB)
MP4
3 Kubernetes 101 Part 2.mp4 (427.46 MB)
MP4
1 Introduction to Version Control Systems.mp4 (68.65 MB)
MP4
10 Merging.mp4 (74.44 MB)
MP4
11 Checking Out Commits.mp4 (57.74 MB)
MP4
12 Git Hosting Services.mp4 (39.16 MB)
MP4
13 Working with Remote Repositories.mp4 (73.33 MB)
MP4
14 Cloning and Delete Branches.mp4 (58.39 MB)
MP4
15 3 way merge.mp4 (74.31 MB)
MP4
16 Summary.mp4 (24.74 MB)
MP4
2 Getting Started with git.mp4 (58.4 MB)
MP4
3 Local Repo vs Remote Repo.mp4 (64.09 MB)
MP4
4 Git Configurations.mp4 (28.36 MB)
MP4
5 Getting Started with Local Repo.mp4 (59.17 MB)
MP4
6 Concept of Working Directory - Staging Area - Commit.mp4 (60.77 MB)
MP4
7 Git Workflow - Local Repo.mp4 (87.57 MB)
MP4
8 Git Branch.mp4 (110.66 MB)
MP4
9 Switching the Branches.mp4 (64.99 MB)
MP4
1 YAML Crash Course.mp4 (106.9 MB)
MP4
1 Introduction to Packaging the ML Models.mp4 (47.39 MB)
MP4
10 Data Preprocessing part 2.mp4 (34.4 MB)
MP4
11 sklearn pipeline.mp4 (103.09 MB)
MP4
12 Training Pipeline.mp4 (83.35 MB)
MP4
13 Prediction Pipeline.mp4 (60.65 MB)
MP4
14 Fixes on Python Scripts.mp4 (36.31 MB)
MP4
15 Add Python Path to MacOS.mp4 (30.5 MB)
MP4
16 Perform Training and Predictions.mp4 (28.76 MB)
MP4
17 Requirements txt file.mp4 (44.62 MB)
MP4
18 Testing the New Virtual Environments.mp4 (35.48 MB)
MP4
19 Create Python tests.mp4 (122.87 MB)
MP4
2 Typical Experimentation with Dataset.mp4 (187.3 MB)
MP4
20 Running Pytest.mp4 (61.98 MB)
MP4
21 Create Manifest file.mp4 (42.45 MB)
MP4
22 Create Version File.mp4 (20.53 MB)
MP4
23 Create setup py.mp4 (86.53 MB)
MP4
24 Packagiing the ML Model & testing.mp4 (136.7 MB)
MP4
25 Summary.mp4 (48.29 MB)
MP4
3 Model fit and generate Predictions.mp4 (20.93 MB)
MP4
4 Challenges in Working inside the Jupyter Notebook.mp4 (134.88 MB)
MP4
5 Understanding the Modular Programming.mp4 (123.45 MB)
MP4
6 Creating Folder Hierarchy for ML Project.mp4 (138.86 MB)
MP4
7 Create Config Module.mp4 (172.16 MB)
MP4
8 Data Handling Module.mp4 (64.38 MB)
MP4
9 Data Preprocessing part 1.mp4 (194.14 MB)
MP4
1 Introduction to Mlflow.mp4 (85.43 MB)
MP4
10 Setting Up MySql Database Locally.mp4 (57.81 MB)
MP4
11 Log Model Metrics in MySql.mp4 (129.98 MB)
MP4
12 Register the Model & Serve the Model.mp4 (114.17 MB)
MP4
13 Summary.mp4 (10.7 MB)
MP4
2 Getting System Ready with mlflow.mp4 (36.77 MB)
MP4
3 Logging Functions of Mlflow Tracking.mp4 (82.61 MB)
MP4
4 Basic Mlflow tutorial.mp4 (136.4 MB)
MP4
5 Exploration of mlflow.mp4 (52.61 MB)
MP4
6 Machine Learning Experiement on MLFlow.mp4 (147.48 MB)
MP4
7 Create ML Model for Loan Prediction.mp4 (95.15 MB)
MP4
8 MLFlow Project.mp4 (158.6 MB)
MP4
9 MLFlow Models.mp4 (118.08 MB)
MP4
1 Docker for Machine Learning.mp4 (40.29 MB)
MP4
2 Introduction to Docker.mp4 (199.33 MB)
MP4
3 Installation of Docker Desktop.mp4 (42.2 MB)
MP4
4 Working with Docker.mp4 (136.14 MB)
MP4
5 Running the Docker Container.mp4 (83.91 MB)
MP4
6 Working with Dockerfile.mp4 (93.84 MB)
MP4
7 Push the Docker Image to DockerHub.mp4 (23.16 MB)
MP4
8 Dockerize the ML Model.mp4 (98.63 MB)
MP4
9 Packaging the training code in Docker Environment & Summary.mp4 (70.89 MB)
MP4
1 What is API, REST and REST API.mp4 (73 MB)
MP4
2 How REST API Works.mp4 (103.1 MB)
MP4
3 What is FastAPI.mp4 (49.78 MB)
MP4
4 Crash course on FastAPI.mp4 (141.94 MB)
MP4
5 Data Validation with Pydantic.mp4 (44.24 MB)
MP4
6 Deploying the Machine Learning Model with FastAPI.mp4 (73.28 MB)
MP4
1 Introduction to Streamit.mp4 (27.36 MB)
MP4
2 Hands On Working with Streamlit.mp4 (125.42 MB)
MP4
3 Building the ML Model with Streamlit.mp4 (337.92 MB)
MP4

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Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

Bitte Anmelden oder Registrieren um Code Inhalt zu sehen!

 

[0dayddl]

​

Download Free Download : MLOps Bootcamp Mastering AI Operations for Success - AIOps
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:15.97 GB
​

Files Included :

Spoiler

1 What and Why MLOps.mp4 (82.88 MB)
MP4
2 The Stages of MLOps.mp4 (136.31 MB)
MP4
1 Agenda of this section.mp4 (11.28 MB)
MP4
2 Linux Features & Bash.mp4 (205.15 MB)
MP4
3 How to Launch EC2 Instances (Quick Refresh).mp4 (52.34 MB)
MP4
4 Basic Linux Commands of Linux.mp4 (607.07 MB)
MP4
1 Introduction to Jenkins.mp4 (125.52 MB)
MP4
10 Installation of Docker in EC2 Instance.mp4 (42.23 MB)
MP4
11 Configure Github Repo - Webhook - Jenkins Credentials.mp4 (139.26 MB)
MP4
12 Introduction to Jenkins FreeStyle Projects and Pipeline Jobs.mp4 (38.29 MB)
MP4
13 Exploration of Jenkins UI.mp4 (18.52 MB)
MP4
14 Create your first First Jenkins Project.mp4 (35.52 MB)
MP4
15 Test Github Webhook with Jenkins.mp4 (105.66 MB)
MP4
16 Installation of Docker Plugin & System Readiness.mp4 (68.28 MB)
MP4
17 Setup Email Notification with Gmail.mp4 (104.77 MB)
MP4
18 Introduction to CI CT CD Pipeline.mp4 (13.59 MB)
MP4
19 Create CI CT CD Pipeline - Github Dockerhub.mp4 (135.47 MB)
MP4
2 How do we Use Jenkins in MLOps.mp4 (19.14 MB)
MP4
20 Create CI CT CD Pipeline - Training.mp4 (57.06 MB)
MP4
21 Create CI CT CD Pipeline - Testing.mp4 (44.78 MB)
MP4
22 Create CI CT CD Pipeline - Deployment.mp4 (45.3 MB)
MP4
23 Perform Test of Pipeline.mp4 (28.23 MB)
MP4
24 Summary.mp4 (41.35 MB)
MP4
3 Prepare and Package ML Model.mp4 (54.23 MB)
MP4
4 Deploy as API with FASTAPI.mp4 (147.76 MB)
MP4
5 Test FastAPI App.mp4 (99.17 MB)
MP4
6 Create Dockerfile.mp4 (44.57 MB)
MP4
7 Exposing the Application Port as per Dockerfile.mp4 (14.73 MB)
MP4
8 Test Locally using Docker Containers.mp4 (103.83 MB)
MP4
9 Installation of Jenkins on AWS EC2 Instances.mp4 (98.69 MB)
MP4
1 Why Monitoring Machine Learning Models is Important.mp4 (50.54 MB)
MP4
2 What is Monitoring of ML models & When to Update Model in Production.mp4 (22.79 MB)
MP4
3 Why Monitoring Machine Learning Models is Difficult.mp4 (92.2 MB)
MP4
4 Challenge - Who Owns what.mp4 (36.78 MB)
MP4
5 Functional Level Monitoring.mp4 (102.28 MB)
MP4
6 Model Drift.mp4 (64.18 MB)
MP4
7 Operational Level Monitoring.mp4 (24.35 MB)
MP4
8 Tools and Best Practices of Machine Learning Model Monitoring.mp4 (19.45 MB)
MP4
1 Introduction to Continuous Monitoring.mp4 (63.9 MB)
MP4
10 Exploring the Basic Querying Prometheus.mp4 (65.9 MB)
MP4
11 Monitor the Infrastructure with Prometheus.mp4 (29.63 MB)
MP4
12 Monitor the Linux Server with Node Exporter.mp4 (92.67 MB)
MP4
13 Monitor the Client Application using Prometheus.mp4 (28.56 MB)
MP4
14 Monitor the FastAPI Application using Prometheus.mp4 (106.88 MB)
MP4
15 Monitor All EndPoints using Prometheus.mp4 (58.93 MB)
MP4
16 Create Visualization with Grafana.mp4 (123.74 MB)
MP4
17 Trigger Alerts with Grafana.mp4 (105.24 MB)
MP4
2 Use case on Continuous Monitoring.mp4 (27.08 MB)
MP4
3 Introduction to Prometheus.mp4 (29.65 MB)
MP4
4 Architecture of Prometheus.mp4 (88.98 MB)
MP4
5 Metric Types of Prometheus.mp4 (29.89 MB)
MP4
6 Installation of Prometheus.mp4 (117.41 MB)
MP4
7 Introduction Grafana.mp4 (12.56 MB)
MP4
8 Installation of Grafana.mp4 (44.36 MB)
MP4
9 Prometheus Configuration file.mp4 (75.68 MB)
MP4
1 Introduction to Docker Compose.mp4 (27.08 MB)
MP4
2 Hands On - Docker Compose with Flask Application.mp4 (157.13 MB)
MP4
3 Hands On - Docker Compose Prometheus Grafana.mp4 (139.07 MB)
MP4
1 Architecture of ML Application Monitoring.mp4 (43.96 MB)
MP4
2 Hands On Monitoring of ML Application using Prometheus.mp4 (130.12 MB)
MP4
1 Introduction to ML Monitoring.mp4 (63.17 MB)
MP4
2 Setting Up WhyLabs.mp4 (13.75 MB)
MP4
3 Whylogs - Drift Detection, Input, Output, Bias Monitoring.mp4 (398.1 MB)
MP4
4 WhyLogs - Constraints and Drift Reports.mp4 (96.61 MB)
MP4
5 Summary.mp4 (14.9 MB)
MP4
1 Post-Productionalizing ML Models - What Next.mp4 (28.26 MB)
MP4
2 Model Security.mp4 (13.05 MB)
MP4
3 Adversarial Attack.mp4 (16.95 MB)
MP4
4 Data Poisoning Attack.mp4 (4.42 MB)
MP4
5 Distributed Denial of Service Attack (DDOS).mp4 (4.08 MB)
MP4
6 Data Privacy Attack.mp4 (9.48 MB)
MP4
7 How to Mitigate Risk of Model Attacks.mp4 (22.42 MB)
MP4
8 AB Testing.mp4 (37.38 MB)
MP4
9 Future of MLOps.mp4 (18.31 MB)
MP4
1 What do we cover in this section.mp4 (11.62 MB)
MP4
10 Creation of S3 Bucket from Console.mp4 (59.79 MB)
MP4
11 Creation of S3 Bucket from CLI.mp4 (38.36 MB)
MP4
12 Version Enablement in S3.mp4 (45.27 MB)
MP4
13 Introduction EC2 instances.mp4 (43.98 MB)
MP4
14 Launch EC2 instance & SSH into EC2 Instances.mp4 (63.24 MB)
MP4
15 Clean Up Activity.mp4 (7.24 MB)
MP4
2 Create AWS Account.mp4 (46.52 MB)
MP4
3 Setting up MFA on Root Account.mp4 (59.01 MB)
MP4
4 Create IAM Account and Account Alias.mp4 (57.25 MB)
MP4
5 Setup CLI with Credentials.mp4 (40.59 MB)
MP4
6 IAM Policy.mp4 (19.33 MB)
MP4
7 IAM Policy generator & attachment.mp4 (59.02 MB)
MP4
8 Delete the IAM User.mp4 (8.87 MB)
MP4
9 S3 Bucket and Storage Classes.mp4 (128.72 MB)
MP4
1 Introduction to Numpy Library.mp4 (47.95 MB)
MP4
10 Linear Algebra Functions in Numpy.mp4 (16.59 MB)
MP4
11 Shape Modification of Arrays.mp4 (50.99 MB)
MP4
12 np arange().mp4 (17.57 MB)
MP4
13 Relational Operators & Aggregation Functions on Numpy Arrays.mp4 (35.8 MB)
MP4
14 Boolean Masking.mp4 (9.16 MB)
MP4
15 Broadcasting on Numpy Arrays.mp4 (120.64 MB)
MP4
16 Summary of Numpy Library Journey.mp4 (18.9 MB)
MP4
17 Introduction to Pandas.mp4 (18.13 MB)
MP4
18 Working with Pandas Series.mp4 (55.53 MB)
MP4
19 Mathematical Operation on Pandas Series.mp4 (14.09 MB)
MP4
2 Basics of numpy array object.mp4 (15.11 MB)
MP4
20 Dataframes in Pandas.mp4 (85.35 MB)
MP4
21 Working with Data in Pandas DataFrame.mp4 (64.24 MB)
MP4
22 Combining the DataFrames.mp4 (76.98 MB)
MP4
23 Other Functions on Pandas DataFrame.mp4 (66.74 MB)
MP4
24 Advanced Functions in Pandas DataFrame.mp4 (136.31 MB)
MP4
25 Introduction to EDA.mp4 (16.23 MB)
MP4
26 Accessing Google Colab.mp4 (23.9 MB)
MP4
27 Loading the Large Dataset for Working.mp4 (48.36 MB)
MP4
28 Preliminary Analysis on DataFrame.mp4 (76.2 MB)
MP4
29 Null values in the Dataframe.mp4 (53.22 MB)
MP4
3 Import Numpy & Access help.mp4 (20.98 MB)
MP4
30 Data Cleaning.mp4 (67.55 MB)
MP4
31 Introduction to Data Visualization.mp4 (62.17 MB)
MP4
32 Matplotlib Basics.mp4 (51 MB)
MP4
33 Types of Plot - Line plot.mp4 (13.45 MB)
MP4
34 Line Plots Hands On.mp4 (48.39 MB)
MP4
35 Adjusting the Plots.mp4 (50.11 MB)
MP4
36 Plot Adjustment Hands On.mp4 (42.54 MB)
MP4
37 Scatter Plot.mp4 (18.4 MB)
MP4
38 Scatter Plot hands on.mp4 (56.78 MB)
MP4
39 Historgram Plot.mp4 (29.51 MB)
MP4
4 Creation of Array Object - np array().mp4 (22.35 MB)
MP4
40 Introduction to Seaborn.mp4 (17.55 MB)
MP4
41 Exploring the data.mp4 (69.93 MB)
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42 Univariate & Bivariate Plots - Continuous Data.mp4 (74.13 MB)
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43 Plot - Categorical Data.mp4 (50.39 MB)
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44 Advanced Plots in Seaborn.mp4 (39.58 MB)
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45 Which Plot to use.mp4 (27.6 MB)
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5 Attributes of Numpy Array.mp4 (15.08 MB)
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6 Array Indexing and Slicing.mp4 (45.48 MB)
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7 Array Creation Functions.mp4 (59.27 MB)
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8 Copy Arrays.mp4 (21.16 MB)
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9 Mathematical Operation on Numpy Arrays.mp4 (19.22 MB)
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1 About the Section.mp4 (14.42 MB)
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10 Operators in Python Programming Language.mp4 (56.69 MB)
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11 Collection - Strings.mp4 (76.84 MB)
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12 Python String - Builtin Functions - Hands On.mp4 (28.93 MB)
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13 Data Structures - List.mp4 (47.96 MB)
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14 Data Structures - Tuples.mp4 (28.68 MB)
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15 Data Structures - Dictionary.mp4 (28 MB)
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16 Data Structures - Sets.mp4 (26.91 MB)
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17 Explicit and Implicit Casting in Python Programming.mp4 (23.93 MB)
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18 Reading the Input from Keyboard.mp4 (21.33 MB)
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19 String Formatting.mp4 (28.15 MB)
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20 Control Statements - Conditional Statements in Python.mp4 (36.28 MB)
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21 Control Statements - Looping Statements.mp4 (67.02 MB)
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22 List comprehension.mp4 (25.74 MB)
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23 Functions.mp4 (54.74 MB)
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24 Modules in Python.mp4 (26.94 MB)
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25 Classes in Python.mp4 (57.71 MB)
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26 File Handling in Python.mp4 (49.54 MB)
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27 Working with Python Scripts.mp4 (13.53 MB)
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28 Libraries in Python.mp4 (17.05 MB)
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3 Introduction to Python Programming.mp4 (35.55 MB)
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4 Install Anaconda.mp4 (13.87 MB)
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5 Hello World - Python.mp4 (32.45 MB)
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6 Jupyter Lab Quick Tour.mp4 (38.91 MB)
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7 Variables in Python.mp4 (15.37 MB)
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8 Variables - Comments - Markdown Cells - Hands On.mp4 (53.37 MB)
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9 Python Literals - Hands On.mp4 (60.76 MB)
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1 MLOps with MLFlow in 1 Hour.mp4 (427.52 MB)
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2 Kubernetes 101 Part 1.mp4 (208.21 MB)
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3 Kubernetes 101 Part 2.mp4 (427.46 MB)
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1 Introduction to Version Control Systems.mp4 (68.65 MB)
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10 Merging.mp4 (74.44 MB)
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11 Checking Out Commits.mp4 (57.74 MB)
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12 Git Hosting Services.mp4 (39.16 MB)
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13 Working with Remote Repositories.mp4 (73.33 MB)
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14 Cloning and Delete Branches.mp4 (58.39 MB)
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15 3 way merge.mp4 (74.31 MB)
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16 Summary.mp4 (24.74 MB)
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2 Getting Started with git.mp4 (58.4 MB)
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3 Local Repo vs Remote Repo.mp4 (64.09 MB)
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4 Git Configurations.mp4 (28.36 MB)
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5 Getting Started with Local Repo.mp4 (59.17 MB)
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6 Concept of Working Directory - Staging Area - Commit.mp4 (60.77 MB)
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7 Git Workflow - Local Repo.mp4 (87.57 MB)
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8 Git Branch.mp4 (110.66 MB)
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9 Switching the Branches.mp4 (64.99 MB)
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1 YAML Crash Course.mp4 (106.9 MB)
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1 Introduction to Packaging the ML Models.mp4 (47.39 MB)
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10 Data Preprocessing part 2.mp4 (34.4 MB)
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11 sklearn pipeline.mp4 (103.09 MB)
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12 Training Pipeline.mp4 (83.35 MB)
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13 Prediction Pipeline.mp4 (60.65 MB)
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14 Fixes on Python Scripts.mp4 (36.31 MB)
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15 Add Python Path to MacOS.mp4 (30.5 MB)
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16 Perform Training and Predictions.mp4 (28.76 MB)
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17 Requirements txt file.mp4 (44.62 MB)
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18 Testing the New Virtual Environments.mp4 (35.48 MB)
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19 Create Python tests.mp4 (122.87 MB)
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2 Typical Experimentation with Dataset.mp4 (187.3 MB)
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20 Running Pytest.mp4 (61.98 MB)
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21 Create Manifest file.mp4 (42.45 MB)
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22 Create Version File.mp4 (20.53 MB)
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23 Create setup py.mp4 (86.53 MB)
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24 Packagiing the ML Model & testing.mp4 (136.7 MB)
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25 Summary.mp4 (48.29 MB)
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3 Model fit and generate Predictions.mp4 (20.93 MB)
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4 Challenges in Working inside the Jupyter Notebook.mp4 (134.88 MB)
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5 Understanding the Modular Programming.mp4 (123.45 MB)
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6 Creating Folder Hierarchy for ML Project.mp4 (138.86 MB)
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7 Create Config Module.mp4 (172.16 MB)
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8 Data Handling Module.mp4 (64.38 MB)
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9 Data Preprocessing part 1.mp4 (194.14 MB)
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1 Introduction to Mlflow.mp4 (85.43 MB)
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10 Setting Up MySql Database Locally.mp4 (57.81 MB)
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11 Log Model Metrics in MySql.mp4 (129.98 MB)
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12 Register the Model & Serve the Model.mp4 (114.17 MB)
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13 Summary.mp4 (10.7 MB)
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2 Getting System Ready with mlflow.mp4 (36.77 MB)
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3 Logging Functions of Mlflow Tracking.mp4 (82.61 MB)
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4 Basic Mlflow tutorial.mp4 (136.4 MB)
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5 Exploration of mlflow.mp4 (52.61 MB)
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6 Machine Learning Experiement on MLFlow.mp4 (147.48 MB)
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7 Create ML Model for Loan Prediction.mp4 (95.15 MB)
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8 MLFlow Project.mp4 (158.6 MB)
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9 MLFlow Models.mp4 (118.08 MB)
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1 Docker for Machine Learning.mp4 (40.29 MB)
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2 Introduction to Docker.mp4 (199.33 MB)
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3 Installation of Docker Desktop.mp4 (42.2 MB)
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4 Working with Docker.mp4 (136.14 MB)
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5 Running the Docker Container.mp4 (83.91 MB)
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6 Working with Dockerfile.mp4 (93.84 MB)
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7 Push the Docker Image to DockerHub.mp4 (23.16 MB)
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8 Dockerize the ML Model.mp4 (98.63 MB)
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9 Packaging the training code in Docker Environment & Summary.mp4 (70.89 MB)
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1 What is API, REST and REST API.mp4 (73 MB)
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2 How REST API Works.mp4 (103.1 MB)
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3 What is FastAPI.mp4 (49.78 MB)
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4 Crash course on FastAPI.mp4 (141.94 MB)
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5 Data Validation with Pydantic.mp4 (44.24 MB)
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6 Deploying the Machine Learning Model with FastAPI.mp4 (73.28 MB)
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1 Introduction to Streamit.mp4 (27.36 MB)
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2 Hands On Working with Streamlit.mp4 (125.42 MB)
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3 Building the ML Model with Streamlit.mp4 (337.92 MB)
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Free search engine download: MLOps Bootcamp Mastering AI Operations for Success - AIOps

 

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Mlops Bootcamp: Mastering Ai Operations For Success - Aiops
Last updated 8/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 11.69 GB | Duration: 35h 21m​

Unlock success in AI Operations with our MLOps Bootcamp - mastering tools,techniques, AIOps for cutting-edge expertise

What you'll learn
Develop a solid foundation in Python, tailored for MLOps applications.
Streamline Machine Learning processes using Python's powerful capabilities.
Leverage Python for effective data manipulation and analysis in Data Science.
Understand how Python enhances the entire data science lifecycle.
Master version control using Git for collaborative development.
Learn to manage and track changes efficiently within MLOps projects.
Dive into the art of packaging Machine Learning models for easy deployment.
Ensure models are reproducible and deployable in diverse environments.
Effectively manage and track Machine Learning experiments using MLflow.
Utilize MLflow for enhanced experiment tracking and management.
Acquire essential skills in YAML for MLOps configuration and deployment.
Gain practical experience in writing and interpreting YAML files.
Explore Docker and its role in containerizing Machine Learning applications.
Understand the advantages of containerization for efficient MLOps.
Develop Machine Learning applications with FastAPI for efficient and scalable deployments.
Explore Streamlit and Flask for creating interactive web applications.
Implement Continuous Integration and Continuous Deployment pipelines for MLOps.
Automate development, testing, and deployment of ML models.
Gain a solid understanding of the Linux operating system.
Explore how Linux is essential for both DevOps and Data Scientists in MLOps.
Dive into Jenkins, an open-source automation server.
Learn to set up and configure Jenkins for automating MLOps workflows.
Develop insights into effective monitoring and debugging strategies for MLOps.
Utilize tools and techniques to identify and address issues in ML systems.
Set up continuous monitoring for MLOps using Prometheus and Grafana
Enhance observability in Machine Learning applications.
Extend Docker skills by mastering Docker Compose.
Learn to deploy multi-container applications seamlessly.
Explore tools and strategies for ongoing performance monitoring in MLOps.
Proactively address issues in production ML systems.
Utilize WhyLogs for efficient monitoring and logging of ML data.
Enhance the observability and traceability of ML systems.
Understand crucial steps for maintaining and updating ML models in a production environment.
Implement best practices for ensuring the long-term success of deployed ML systems.

Requirements
Familiarity with programming concepts is preferred, but we cover in our course as well
Some knowledge of data manipulation and analysis will be beneficial.
Basic understanding of version control concepts, preferably with Git - will be beneficial
Enthusiasm for the intersection of Machine Learning and DevOps practices.
Participants should have access to a computer with a stable internet connection for viewing video content and engaging in practical exercises.

Description
Welcome to our extensive MLOps Bootcamp (AI Ops Bootcamp), a transformative learning journey designed to equip you with the skills and knowledge essential for success in the dynamic field of Machine Learning Operations (MLOps). This comprehensive program covers a diverse range of topics, from Python and Data Science fundamentals to advanced Machine Learning workflows, Git essentials, Docker for Machine Learning, CI/CD pipelines, and beyond.Curriculum Overview:1. Python for MLOpsive into the fundamentals of Python tailored specifically for MLOps.Explore Python's role in streamlining and enhancing Machine Learning processes.Develop proficiency in leveraging Python for effective MLOps practices.2. Python for Data Science:Uncover the power of Python in the context of Data Science.Learn essential data manipulation and analysis techniques using Python.Understand how Python enhances the entire data science lifecycle.3. Git and GitHub Fundamentals:Master the essentials of version control with Git.Understand how GitHub facilitates collaborative development in MLOps.Learn to manage and track changes effectively within MLOps projects.4. Packaging the ML Modelselve into the art of packaging Machine Learning models.Explore different packaging techniques and their implications.Ensure your ML models are easily deployable and reproducible.5. MLflow - Manage ML Experiments:Learn to effectively manage and track Machine Learning experiments.Understand the features and benefits of MLflow for experiment tracking and management.Implement MLflow in your MLOps projects for enhanced experimentation.6. Crash Course on YAML:Acquire a solid foundation in YAML, a key configuration language.Learn how YAML is used in MLOps for configuration and deployment.Gain practical skills in writing and interpreting YAML files.7. Docker for Machine Learning:Explore Docker and its role in containerizing Machine Learning applications.Understand the advantages of containerization for MLOps.Learn to build and deploy Docker containers for Machine Learning projects.8. Build MLApps using FastAPI: Dive into FastAPI, a modern, fast web framework for building APIs.Learn to develop ML applications using FastAPI for efficient and scalable deployments. Implement best practices for building robust MLApps.9. Build MLApps using Streamlit: Explore Streamlit, a powerful framework for creating interactive web applications. Develop hands-on experience in building MLApps with Streamlit. Understand how Streamlit enhances the user interface for Machine Learning applications.10. Build MLApps using Flask:Gain proficiency in Flask, a popular web framework for Python. Learn to build and deploy Machine Learning applications using Flask. Understand the integration of Flask with MLOps workflows.11. CI/CD for Machine Learning: Explore Continuous Integration and Continuous Deployment (CI/CD) pipelines in the context of MLOps. Implement automation to streamline the development, testing, and deployment of ML models. Learn to build robust CI/CD workflows for Machine Learning projects.12. Linux Operating System for DevOps and Data Scientists: Understand the fundamentals of the Linux operating system. Explore how Linux is essential for both DevOps and Data Scientists in MLOps. Gain practical skills in working with Linux for MLOps tasks.13. Working with Github Actions: Dive into Github ActionsLearn to set up and configure Github actions for automating MLOps workflows. Understand how Github Actions enhances the efficiency of continuous integration and deployment in MLOps.14. Monitoring and Debugging of ML System: Gain insights into effective monitoring and debugging strategies for MLOps. Learn tools and techniques to identify and address issues in Machine Learning systems.Implement best practices for maintaining the health and performance of ML systems.15. Continuous Monitoring with Prometheus: Explore Prometheus, an open-source monitoring and alerting toolkit. Learn to set up continuous monitoring for MLOps using Prometheus. Understand how Prometheus enhances observability in Machine Learning applications.16. Deploy Applications with Docker Compose: Extend your Docker skills by mastering Docker Compose. Learn to deploy multi-container applications seamlessly using Docker Compose.Understand how Docker Compose enhances the deployment of complex MLOps architectures.17. Continuous Monitoring of Machine Learning Application: Dive into continuous monitoring practices specifically tailored for Machine Learning applications. Explore tools and strategies to ensure ongoing performance monitoring in MLOps.Implement solutions for proactively addressing issues in production ML systems.18. Monitor the ML System with WhyLogs: Explore WhyLogs, a data logging library for Machine Learning.Learn how WhyLogs facilitates efficient monitoring and logging of ML data.Implement WhyLogs to enhance the observability and traceability of your ML system.19. Post Productionizing ML Models: Understand the crucial steps involved in post-productionizing Machine Learning models. Explore strategies for maintaining and updating ML models in a production environment. Gain insights into best practices for ensuring the long-term success of deployed ML systems.Conclusion: Embark on this comprehensive MLOps Bootcamp to transform your skills and elevate your proficiency in the dynamic and ever-evolving field of Machine Learning Operations. Whether you are a seasoned professional or just starting your journey in MLOps, this program provides the knowledge, tools, and practical experience needed to succeed in implementing robust and efficient Machine Learning workflows. Join us and become a master of MLOps, ready to tackle the challenges of the modern AI landscape with confidence and expertise.

Who this course is for:
Data scientists seeking to extend their skills into the operational aspects of deploying and maintaining machine learning models.,Software developers interested in mastering the tools and practices for integrating machine learning into real-world applications.,DevOps professionals aiming to specialize in MLOps and enhance their proficiency in deploying and managing machine learning systems.,Data engineers looking to broaden their skill set by incorporating MLOps practices into data pipelines.,IT professionals wanting to understand the integration of machine learning models within operational workflows.,Individuals passionate about the latest advancements in technology and eager to explore the practical aspects of MLOps.,Entrepreneurs and business professionals seeking to understand how MLOps can drive innovation and competitive advantage in their organizations.,Students and researchers in the fields of computer science, data science, and related disciplines looking to expand their knowledge in MLOps.,Individuals transitioning into roles that involve machine learning operations and deployment.,Enthusiasts who are keen to explore the convergence of machine learning and operations, regardless of their current role or background.

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