Machine Learning is a form of AI. It is one of the most predominant topic in Artificial Intelligence. It enables a system to learn from the data rather than programming, though Machine Learning is not a simple process to achieve.
Machine Learning uses a variety of algorithms that helps learn to improve data, describe data, and predict the outcomes.
AI Infrastructure includes the resources, processing and tooling to develop, to train and operate machine learning models. ML infrastructure defines every stage of machine learning.
To understand ML/AI Infrastructure, the above flow graph with building blocks helps to understand the infrastructure development and its components.
Model Selection :
It is the process of selecting a well-fitness model. important aspect in Machine Learning. It shows and defines what data is ingested, which tools are used, what components are needed, and how components are interlinked with each other..
Data Ingestion :
Data Ingestion is nothing but data filtering. filtering of data plays a key role in machine learning as on basis of this we get the output to the Machine Learning model.
ML Pipeline Automation :
ML Pipeline automation is about splitting the data, data transformation, data preparation, training, upto actual deployment. There are many tools which are available to automate machine learning workflows. Pipelines are mainly for processing the data, training the models, performing monitoring tasks, and deploying the results.
Visualisation & Monitoring :
Visualising and monitoring comes under the representation of the dataset and observing and checking the progress or quality. We can know how accurate model training is, and model results.
Model Testing :
Model Testing is a technique where the test cases are obtained from the machine learning model. Testing requires:
- Collection and analysis of qualitative and quantitative data.
- Multiple training runs in the same environments.
- The ability to identify where errors are occurred
Deployment :
Model Deployment comes under the model serving and model conversion. This is the final step.
Inference : Final conclusion formed by above outputs.
ML /AI Infrastructure mainly involves three parts :
- Data Preparation
- Model Building
- Production
(1) Data Preparation :
Data preparation is the method of cleaning and transforming the raw data earlier to processing and analysis. It ensures accuracy in the data. Data is commonly created with missing values, inaccuracies or other errors, so we have to clean that data. Hence Data preparation is an important aspect to get accurate results and finite outputs.
Data Exploration and Processing :
For data exploration and processing, we use websites like Paxata, Trifacta, alteryx, databricks, Superb AI, Iguazia, Qubole, Hive, SAS
These are simple, quick, easy, open and secure platforms. These softwares allows you to prepare complex and visualise the data. These are the data analytics softwares
Data Version Control :
Pachyderm, Dataiku, DVC. These are Open-source version control system for Data Science and Machine Learning projects
Feature Engineering and Storage :
Feature Labs, Feature Tools and google Feast are the software companies where machine learning innovation is done.
Data Labelling :
For this we can use websites like Scale, Labelbox, figure eight, Google AI.
Data Quality Checks :
We use some websites like great expectations.
We use all these websites for Data Preparation itself. All are open sources and working great by giving output of the implemented task finitely.
- For Data Preparation, we can use any of these websites for our task completion.
(2) Model Building :
In model Building, there are many stages like model training, model evaluation, explainability.
Hosted Notebooks :
hosted notebooks like Google Colab , Databricks, AmazonSageMaker , Domino , Deepnote , CLOUDERA , Azure Machine Learning.
Model Management , Version Tracking and Storage :
We can use databricks, AmazonSageMaker, Google AI, Azure Machine Learning, ALGORITHMIA, Domino, dataiku, SAS, iguazio.
Experiment Tracking :
We can use Weights & Biases, comet, TensorBoard, mlflow.
Model optimisation and Hyper Parameter :
We can use SIGOPT, Weights & Biases, comet, anyscale, AmazonSageMaker.
Auto ML :
We use DataRobot , AmazonSageMaker, Google AI , dataiku, Azure Machine Learning
Model Training :
For model training in ML/AI, we can use AmazonSageMaker , Azure Machine Learning , Google AI, Kube flow, anyscale, SAS , iguazio, perceptilabs.
Model Evaluation :
For the model evaluation in Machine Learning or AI we can use TensorBoard or Streamlit.
Model Exlplainability :
For the model Explainability in Machine Learning or AI , we can use either fiddler or TensorBoard.
(3) Production :
Model Obesrvability :
for model observability we use websites lie Arize , Data Robot
Model Compliance and Audit :
For model compliance and Audit , we use websites like fiddler, SAS
Model Deployment and Serving :
For model deployment and serving we use websites like AmazonSageMaker, Kubr flow, ALGORITHMIA, DataRobot, Google AI, Azure Machine Learning, PerceptiLabs, SAS
Model Validation :
For Model Validation , we use websites like aroze, fiddler, SAS
Machine Learning Training Infrastructure:
ML Training infrastructure contains
- Training ( multi-platform Support )
- Experimentation workflow – containerization
- Logging
- DashBoard, Result monitoring
- job Scheduling
- Hyperparameter search ; Distributed training at scale.
Conclusion :
Machine Learning is a vast concept to deal with, it is the first part to achieve when you want to deal with Artificial Intelligence in a very finite and easier way. Machine Learning techniques are required to improve the accuracy of the predictive ML model.
From this article you will be having an idea of what are the sources available to achieve Machine Learning at every stage and in every aspect. So now You got to know that there are many paths to achieve ML/AI infrastructure.
Thanks for Reading ??…!
Written By: Sai Harsha Tamada
Reviewed By: Vikas Bhardwaj
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