IMPLEMENTING ML MODELS THROUGH API.
Machine learning is indeed an indispensable part of data science with numerous applications in several domains such as finance, insurance, health sector, etc but it can be really intimidating for people who are not tech-savvy.
It was a common belief that only people with thorough mathematical skills or big data scientists could only perfectly use machine learning but this is not the case as Machine Learning APIs have facilitated people and made it easier for them to learn and utilize machine learning methodologies.
So, how can you define an API?
An API is an application -programming interface where software or applications talk to each other without the knowledge of the user or any interference by the user.
https://miro.medium.com/max/1000/1*OcmVkcsM5BWRHrg8GC17iw.png
For clear understanding let us take an example when we book tickets for a movie show online, we are required to submit our card details, the website uses an API to confirm your card details by sending your card details to a remote application which in turn verifies whether the processed information is correct or not. After correctly verifying the details and processing the payment approval is sent back to the movie ticket website to issue the tickets. Now as a user we see only one interface but actually, there are many applications working behind the scenes using APIs. Since it is nearly impossible for the user to see when the software functions are transferred from one application to another therefore such type of integration can be termed as seamless integration. Google map is a perfect example of API.
Machine learning focuses on teaching the computer to do various tasks by feeding input data sets and allowing it to find patterns and connections called training. Once the model is trained, new data sets can be given to make decisions.
Several mobile development companies such as Google, Apple, Microsoft, and Amazon use ML API for language processing, voice recognition, vision, data discovery, and understanding human speech and text.
The most popular Machine learning API’s for data science are-
- Amazon Machine Learning API-
Amazon.com released its API built on the Amazon cloud platform to enable website developers to easily access its product information. Using this machine learning API, a third-party website can post direct links to products listed on Amazon with updated prices and “Buy now Option”.
Amazon Machine Learning API is simple and user-friendly and its special features such as algorithmic restrictions, creating UI permission levels, wizard-driven GUI have made this machine learning API a favorite choice among developers.
Examples- Detecting fake users, calculating sales predictions, etc.
- Google Cloud Machine Learning API-
Google Cloud API works on RPC as well as REST. Several cloud features such as Speech API, Vision API, and Natural API are some of the modern world applications. Cloud Vision API application helps the developers to integrate vision features such as image labeling, detecting faces, and objects, reading printed and handwritten text, etc.
Examples-Helps in predicting navigation routes by creating a list of routes and places that the driver usually visits. Detection of fraud can be easily facilitated with the help of API.
- Microsoft Azure Machine learning API-
https://miro.medium.com/max/1127/1*-ganvHfXEbn6oYk-krRpIg.jpeg
This is a Text Analytics API that provides powerful natural language processing features over available raw text. It is a cloud-based algorithm that includes sophisticated machine learning algorithms in simple and visual form. Its main features include key phrase extraction, language detection, sentiment analysis, and named entity recognition.
Examples- Bing, Xbox.
- IBM Watson-
https://infotechlead.com/wp-content/uploads/2020/06/IBM-Watson-Works.jpg
IBM Watson is a powerful machine learning algorithm that has similar offerings to the Google Cloud Platform and permits the developers to identify patterns and trends and other actionable insights. This helps in better decision-making.
The key components of Watson Discovery API are IBM Watson Personality Insights, IBM Watson Assistant, IBM Watson Visual Recognition, IBM Watson Speech to Text, IBM Watson Natural Language Processing.
Examples- Language translations, Making social predictions with the help of given text.
Watson services give users a lot of flexibility but are used at the enterprise level and not for students as it is more costly and requires the use of Bluemix.
SIMPLE STEPS IN DEPLOYING MACHINE LEARNING MODELS TO CLOUD-
https://cloudxlab.com/blog/wp-content/uploads/2019/04/Machine-Learning-Model-Deployment.png
- Getting a dataset- In order to make correct predictions, we got to have a good data set that is not too small. Python has some very amazing libraries such as pandas which help create a proper ML model. The data is first loaded in Panda and then split into its label and features. The label column is then grabbed by its name (quality) and dropped in the column to get all the features.
2. Training a machine learning model- The purpose of the machine learning model is to find the relationship between the label and its features. This is done by showing our model a bunch of examples from our dataset. All these examples define how each feature in our model affects the label. This process is called ‘training the model.’ Using the Scikit-Learn library the model can be trained quickly.
3. Creating a simple web server using Flask- In order to deploy our model, we must first create a web server. Web servers listen to web traffic and perform functions when a request is sent to them. To deploy our model, we first have to create a server. Servers are computers connected to a network and provide service to clients, they listen to web traffic and perform functions when they find a request addressed to them. The function that they run depends on the request’s route and other available data that it has. Once this is done, the server sends a confirmation message back to the requester.
Flask is a powerful python micro web server framework that allows us to build REST API-based web-services quickly with minimum configuration hassle.
4. Adding a model to the server- The next step is to load the trained model to the webserver with the help of the pickle library. We can access the server by sending a post to the “/echo” route. The route grabs a number of features from the request body and delivers them to the model. Once the model has made the prediction it is sent back to the requester.
Cloud computing has grabbed a lot of attention in recent years. The flexibility of cloud computing, it’s instant provisioning, and infinite scale makes it a suitable ML model for training. However, there are certain challenges that cloud computing cannot solve in ML. A few of those challenges are-
- Manually preparing Data- A lot of time and energy is wasted by data scientists as most of them manually write codes. Writing these codes manually is not only time consuming but also difficult and is prone to errors while reusing and maintaining them.
- Removing bias from ML data models- Removing bias or training an ML model requires feeding the model with more data, which is very time-consuming and costly. In case we try and reduce the time required to remove bias the accuracy may be compromised.
- Reusability and Reproducibility- In order to reuse and reproduce data models, all the actions performed on the data sets should be recorded. With the upcoming data privacy law, it might be difficult for companies to answer what they did with their customer's data and why.
- Reimplementation- Another challenge that the IT companies face is the reimplementation of the ML models. Reimplementation of previously developed models leads to a lot of delays and errors because of complex coding requirements thereby increasing the time of implementation and makes the execution of the models unmaintainable.
To conclude, which API for Machine Learning should we choose depends on our project requirement and the requirement of our industry. Amazon is affordable and enables us to test many functions for free, Azure is apt for beginners whereas Google is advantageous with the amount of data that the model is pre-trained with.
About Zorba : Zorba is an end to end Data Science and Machine Learning Consulting Firm. We offer services across consulting and training in Data Science and AI. We have catered to 45+ SME clients and 8 of our clients are fortune 500 companies.
Learn data science with Zorba.
Join our Becoming data leader Cohort for hands-on training by industry experts.
Read more of our work:
References-
https://medium.com/@ODSC/machine-learning-challenges-you-might-not-see-coming-9e3ed893491f https://hackernoon.com/4-ways-startups-can-overcome-implementation-challenges-of-machine-learning-dbgoi384n
