MLOPS Tools available in Market
1. Version Control and Experiment Tracking:
- DVC (Data Version Control): Manages datasets and models using version control, similar to how Git handles code.
- MLflow: An open-source platform to manage the ML lifecycle, including experiment tracking, model versioning, and deployment.
- Weights & Biases: Offers experiment tracking, model management, and visualization tools.
2. Model Deployment:
- Kubeflow: An open-source toolkit that runs on Kubernetes, designed to make deployments scalable and portable.
- AWS SageMaker: Amazon’s fully managed service that provides tools for building, training, and deploying machine learning models at scale
- TensorFlow Serving: A flexible, high-performance serving system for machine learning models, designed for production environments.
3. CI/CD for Machine Learning:
- GitHub Actions: Automates CI/CD pipelines for machine learning projects, integrating with other MLOps tools.
- Jenkins: An automation server that can be customized to manage CI/CD pipelines for machine learning.
4. Model Monitoring and Management:
- Prometheus & Grafana: Combined, they provide powerful monitoring and alerting solutions, often used for ML model monitoring.
- Seldon Core: An open-source platform for deploying, scaling, and managing thousands of machine learning models on Kubernetes.
5. Data Pipeline Management:
- Apache Airflow: An open-source platform to programmatically author, schedule, and monitor workflows.
- Prefect: A modern workflow orchestration tool that handles complex data pipelines, including those involving ML models.
1. Version Control and Experiment Tracking:
- DVC (Data Version Control): Manages datasets and models using version control, similar to how Git handles code.
- MLflow: An open-source platform to manage the ML lifecycle, including experiment tracking, model versioning, and deployment.
- Weights & Biases: Offers experiment tracking, model management, and visualization tools.
2. Model Deployment:
- Kubeflow: An open-source toolkit that runs on Kubernetes, designed to make deployments scalable and portable.
- AWS SageMaker: Amazon’s fully managed service that provides tools for building, training, and deploying machine learning models at scale
- TensorFlow Serving: A flexible, high-performance serving system for machine learning models, designed for production environments.
3. CI/CD for Machine Learning:
- GitHub Actions: Automates CI/CD pipelines for machine learning projects, integrating with other MLOps tools.
- Jenkins: An automation server that can be customized to manage CI/CD pipelines for machine learning.
4. Model Monitoring and Management:
- Prometheus & Grafana: Combined, they provide powerful monitoring and alerting solutions, often used for ML model monitoring.
- Seldon Core: An open-source platform for deploying, scaling, and managing thousands of machine learning models on Kubernetes.
5. Data Pipeline Management:
- Apache Airflow: An open-source platform to programmatically author, schedule, and monitor workflows.
- Prefect: A modern workflow orchestration tool that handles complex data pipelines, including those involving ML models.
10 great Python packages for Data Science not known to many:
1️⃣ CleanLab
Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.
2️⃣ LazyPredict
A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.
3️⃣ Lux
A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.
4️⃣ PyForest
A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.
5️⃣ PivotTableJS
PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥
6️⃣ Drawdata
Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.
7️⃣ black
The Uncompromising Code Formatter
8️⃣ PyCaret
An open-source, low-code machine learning library in Python that automates the machine learning workflow.
9️⃣ PyTorch-Lightning by @LightningAI
Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.
🔟 Streamlit
A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.
1️⃣ CleanLab
Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.
2️⃣ LazyPredict
A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.
3️⃣ Lux
A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.
4️⃣ PyForest
A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.
5️⃣ PivotTableJS
PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥
6️⃣ Drawdata
Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.
7️⃣ black
The Uncompromising Code Formatter
8️⃣ PyCaret
An open-source, low-code machine learning library in Python that automates the machine learning workflow.
9️⃣ PyTorch-Lightning by @LightningAI
Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.
🔟 Streamlit
A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.
PowerBI complete curriculum from basic to advance
Introduction to charts
Filters
Dropdown
Drill down and Drill through in PowerBI
Add images, buttons, bookmarks
Top N
Dax expression
Introduction to Power query for data cleaning
- basic exploration summary statistics
- values count function
- Checking for wrong data entry errors
- Missing values
- Duplicate values
- Outliers
- Checking data imbalance
- data skewness
- Handling json types columns
- Handling time stamp columns
- Feature engineering
- Query folding in PowerBI
Data modelling in PowerBI
- Creating relationships between several tables
- Active vs inactive relationships
- Cardinality and Cross-Filtering
Security and Data governance
- Row level security (static vs dynamic)
- Object level security
- Combining row level and object level security
Other topics
- Real time streaming dashboard in PowerbI
- Data lineage in powerbi
- PowerBI Gateway
- Incremental refresh vs Scheduled refresh in powerbi
- import mode vs direct mode in PowerBI
- native query
- row context and filter context in powerbi
- Powerbi dataflow
Introduction to charts
Filters
Dropdown
Drill down and Drill through in PowerBI
Add images, buttons, bookmarks
Top N
Dax expression
Introduction to Power query for data cleaning
- basic exploration summary statistics
- values count function
- Checking for wrong data entry errors
- Missing values
- Duplicate values
- Outliers
- Checking data imbalance
- data skewness
- Handling json types columns
- Handling time stamp columns
- Feature engineering
- Query folding in PowerBI
Data modelling in PowerBI
- Creating relationships between several tables
- Active vs inactive relationships
- Cardinality and Cross-Filtering
Security and Data governance
- Row level security (static vs dynamic)
- Object level security
- Combining row level and object level security
Other topics
- Real time streaming dashboard in PowerbI
- Data lineage in powerbi
- PowerBI Gateway
- Incremental refresh vs Scheduled refresh in powerbi
- import mode vs direct mode in PowerBI
- native query
- row context and filter context in powerbi
- Powerbi dataflow
These are the following courses that we offered as a training
- Complete Data Science (6 Months)
- Data analytics (3 Months)
- Big data analytics (2 Months)
- Complete MLOPS ( 2 Months)
- AWS Training (2 Months)
- GenAI training (3 Months)
Dm us on whatsapp
+9183182 38637
All are paid training
- Complete Data Science (6 Months)
- Data analytics (3 Months)
- Big data analytics (2 Months)
- Complete MLOPS ( 2 Months)
- AWS Training (2 Months)
- GenAI training (3 Months)
Dm us on whatsapp
+9183182 38637
All are paid training
Accenture Data Scientist Interview Questions!
1st round-
Technical Round
- 2 SQl questions based on playing around views and table, which could be solved by both subqueries and window functions.
- 2 Pandas questions , testing your knowledge on filtering , concatenation , joins and merge.
- 3-4 Machine Learning questions completely based on my Projects, starting from
Explaining the problem statements and then discussing the roadblocks of those projects and some cross questions.
2nd round-
- Couple of python questions agains on pandas and numpy and some hypothetical data.
- Machine Learning projects explanations and cross questions.
- Case Study and a quiz question.
3rd and Final round.
HR interview
Simple Scenerio Based Questions.
Finally
I was offered a CTC of ××× LPA plus Joining Bonus
Credit - Shubhankit
1st round-
Technical Round
- 2 SQl questions based on playing around views and table, which could be solved by both subqueries and window functions.
- 2 Pandas questions , testing your knowledge on filtering , concatenation , joins and merge.
- 3-4 Machine Learning questions completely based on my Projects, starting from
Explaining the problem statements and then discussing the roadblocks of those projects and some cross questions.
2nd round-
- Couple of python questions agains on pandas and numpy and some hypothetical data.
- Machine Learning projects explanations and cross questions.
- Case Study and a quiz question.
3rd and Final round.
HR interview
Simple Scenerio Based Questions.
Finally
I was offered a CTC of ××× LPA plus Joining Bonus
Credit - Shubhankit
Data analytics training curriculum
* Python
* SQL
* NoSQL
* Tableau
* PowerBI
* Excel
* Aws
Aiming for 1000 subscribers on YOUTUBE.
We will upload data analytics for free. It is of 60 hours duration
https://yt.openinapp.co/csqv6
* Python
* SQL
* NoSQL
* Tableau
* PowerBI
* Excel
* Aws
Aiming for 1000 subscribers on YOUTUBE.
We will upload data analytics for free. It is of 60 hours duration
https://yt.openinapp.co/csqv6
yt.openinapp.co
DataSpoof
Hello world, it’s Abhishek! I am a Data Scientist | Corporate Trainer on a mission to teach Artificial intelligence to all my students. Topics including AI, Mathematics, Science, Technology, I simplify these topics to help you understand how they work. Using…
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Aws training review