Why Do We Need AWS Sagemaker?

Did you just binge-watch an entire series again? Have you wondered how online streaming platforms recommend series and movies you enjoy?

This is the magic of Machine learning. Machine learning is a branch of Artificial Intelligence. Artificial intelligence focuses on how machines can perform human-like tasks, whereas Machine learning teaches a machine to create models for particular tasks. Machine learning models use voluminous data as inputs and form a pattern using an algorithm. The pattern is then compared with existing models to determine the accuracy of the prediction. These models are then used to make real-time analyses. Cloud service platforms such as Amazon Sagemaker assist the users in training and deploying Machine learning Models on massive scales.

This article will highlight the key features of AWS Sagemaker and why we need AWS Sagemaker.

Amazon Sagemaker

Amazon Sagemaker is a fully managed service provided by the leading cloud service Amazon Web Service to help data scientists and developers to build, train, deploy machine learning models. You can use it to design a machine learning model from scratch, or you can use the inbuilt algorithm. 

Today, Amazon Sagemaker is used for various purposes, including enhancing data training and interfaces, accelerating production-ready AI models, and designing accurate data models.

ML models comprise three stages – Build, Train, and Deploy. First, data scientists accumulate the required data and analyse the data to build and train ML models. Then, a software engineer deploys the ML model to a full-scale web server. 

The growing scales of ML models make the process complex and tedious, and this is where Amazon Sagemaker comes to the rescue.

How does AWS Sagemaker work?

 Amazon Sagemaker studio is an interpreted development environment for ML platforms. It is a visual interface that provides complete access, control and visibility to build, train, deploy an ML model. You can create new notebooks, create automatic models, debug and model and detect data drifts in Amazon Sagemaker studio.

Build

The first step for creating a machine learning model is assembling data and building the data sets required for the model.

Amazon Sagemaker uses Jupyter notebooks. Jupyter Notebooks are used to create, share codes, equations and multimedia presentations under one file. These hosted notebooks make the visualisation and creation of datasets easier. The data can be stored in Amazon S3. One-click notebooks help in sharing files instantly. 

For example, if your data model is about music recommendation software. You need to collect data. Here, it would be the song name, artist, genre, etc. These datasets are then converted into features using the Sagemaker Data Wrangler. Conversion of Data into features helps in removing noise from the data. This helps build the learning data, an essential requirement for training models.

Train

After assembling and building datasets, we need to train the machine learning model to analyse and make predictions. ML algorithms are required to train data models, known as learning algorithms and learning data. Learning data comprises the data sets that are essential for a particular model. For example, for a series recommendation model, you require data about series, actors, directors, etc. 

AWS Sagemaker has the most common pre-installed built-in algorithms, which you can use as a learning algorithm. Parameters and hyperparameters are tuned to optimize the algorithm. Due to the constant changes made in the model, it becomes difficult to manage the training and track the progress. Amazon Sagemaker helps in monitoring and organizing all the iterations, such as changes in parameters, algorithms and data sets. Sagemaker stores all the iterations as experiments.

AWS Sagemaker also provides a debugger. Debugger detects and fixes any standard error in the model. The Sagemaker Debugger also sends warnings and provides a solution for the problems detected in training.  AWS Tensorflow optimisation helps create meticulous and sophisticated models in a short period.

Deploy

When your training models are ready, it is time to deploy them. Deployment of the model in simple words means making a model available for real-time use with the help of Application Program Interfaces(APIs). When a model is ready to analyze real-time scenarios, we deploy the model using Amazon Sagemaker. Amazon Sagemaker has a model monitor which detects concept drifts.

Concept drift is one of the significant problems for attaining high accuracy. It denotes the gap between the real-time data and the learning data that causes a drift in the prediction. Amazon Sagemaker Model monitor also ensures all models emit key metrics and provides a detailed report which helps in enhancing the model. Amazon Sagemaker also connects the end with HTTPS, which connects with web services (APIs).

As Amazon Sagemaker is a service provided by Amazon Web Service (AWS), it can access other resources provided by AWS. This makes the process of deployment of models on a large scale easy. One such service is Amazon Elastic Interface, which reduces the machine learning inference cost by seventy per cent.

Features of AWS Sagemaker

Amazon Sagemaker provides many features that make creating machine learning models effortless. Some of the features are:

1. Amazon Sagemaker Datawrangler:

Enables us to convert data into features by using built-in data transformation. 

 2. Amazon Sagemaker Clarify:

Amazon Sagemaker Clarify provides transparency.it provides bias detection during and after the training to improve the data models. 

 3. Amazon Sagemaker Ground Truth:

Amazon Sagemaker Ground Truth helps in data labelling and creating meticulous data models. As a result, data labelling costs in high scale machine learning projects can be significantly reduced. 

4. Amazon Sagemaker Features Store:

Amazon Sagemaker Features Store is a built-in function where you can store, share and discover the features you have created. It also has ML features in real-time and in batch.

5. Amazon Sagemaker Built-in Notebook:

Amazon Sagemaker Built-in Notebooks are Jupyter notebooks. These notebooks are used for building and sharing codes, equations, and multimedia presentations. These are stored in the same place and are easily accessible. 

6. Amazon Sagemaker Autopilot:

amazon Sagemaker Autopilot enables you to automatically build, train, and deploy machine learning models. It provides complete transparency and control over your project.

7. Amazon Sagemaker Experiments:

Amazon Sagemaker Experiments helps you store all the iterations made during the training of a model. You can access previous and active experiments, and you can also compare them for better results.

8. Amazon Sagemaker Debugger

Amazon Sagemaker Debbuger helps the user detect and debug errors in the model before the deployment of the model.

9. Amazon Sagemaker Pipelines

Amazon Sagemaker Pipelines creates a workflow for the entire machine learning model.

The workflow consists of data preparations and model training and deployment.

10. Amazon Sagemaker Model Monitor

To create accurate real-time models, we need to monitor concept drifts. This is possible because of Amazon Sagemaker Model Monitor.

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Summary

Amazon Sagemaker has a range of features that helps us to create and enhance the productivity of machine learning models in no time. It reduces the cost of making a machine learning model by seventy per cent as it’s pretty fast and highly scalable.

This makes Amazon Sagemaker one of the best cloud service platforms for ML.

Amazon Sagemaker is just a tool for creating a machine learning model – you’ll have to use it to fit your needs if you are looking to kickstart your machine learning career.

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