AI Developer Toolbox: Essential Resources for Success

Artificial Intelligence is no longer just a subject of speculative fiction or academic research; it has evolved into a pivotal force driving innovation across multiple industries. 

From streamlining operations in healthcare to revolutionising customer experiences in ecommerce, AI’s potential to automate and enhance is unparalleled. Recognising the crucial role AI plays, developers and businesses must be equipped with the right tools and resources. 

To navigate this landscape, we turned to insights from Roshan Manandhar, a seasoned solution architect at EB Pearls, who brings a wealth of experience in implementing AI strategies effectively. In this article, we delve into the indispensable tools and resources that are fundamental for any AI developer. 

Machine learning frameworks are necessary to construct and implement AI models. Preliminary data processing, model development, and evaluation are all made easier with these frameworks, which is why they are crucial for an AI engineer or developer.

Created by Meta AI and now owned by the Linux Foundation, PyTorch is a machine learning toolkit that builds on the Torch library. It is utilised in software engineering for tasks like image recognition in computer science and natural language processing.

When it comes to its usability, Roshan says “If the project is complex, that requires experimenting with different neural network architectures then PyTorch can be a better choice (than other tools) due to its flexibility and dynamic computation graph.”

“TensorFlow can be a great choice due to its performance optimisation techniques and flexibility in deploying across different platforms.”

This tool is an AI and machine learning package that is both free and open-source. Although it has many potential applications, its primary focus is on deep neural network training and inference. The Google Brain group built it for internal usage in Google’s R&D and manufacturing processes.

Scikit-learn is another free software machine learning library, this time for the Python programming language. 

From his own experience, Roshan states “If traditional machine learning algorithms such as decision trees or logistic regression are enough for the project, then scikit-learn can be used because of its simplicity.”

Natural language processing (NLP) tools are collections of programs and libraries that make it possible for computers to comprehend, analyse, and even create new languages. “For tasks like tokenization, stemming, sentiment analysis, and entity recognition these libraries can be really helpful. These tools provide pre-build libraries and functions to facilitate the aforementioned tasks.”

The Natural Language Toolkit (NLTK) is a collection of Python-based tools and programs for statistical analysis and symbolic natural language processing, with an emphasis on English. It has features that allow for semantic reasoning, tagging, parsing, categorisation, stemming, and tokenisation.

AI developers can process textual data with the help of the Python package TextBlob. “For projects that involve extracting entities from text, TextBlob is used to tokenise the text and extract relevant entities, which saves us a lot of time compared to writing our own code.”

Software programs known as data visualisation tools simplify the process of interpreting and discovering insights from large datasets by converting them into graphical representations like charts, graphs, and maps.

Tableau is a software designed for interactive data visualisation in business intelligence, enabling users to create visually appealing charts, graphs, and dashboards for data analysis. “If our motive is to create an interactive and shareable dashboard on the web, then Tableau Public, the free version of Tableau, can be a good option.”

Roshan quotes this as his preferred tool for data visualisation when working on projects. The Python programming language and its numerical mathematics extension NumPy both make use of Matplotlib, a package that allows for graphing. 

For programs that use general-purpose GUI toolkits such as Tkinter, wxPython, Qt, or GTK, it offers an object-oriented API for integrating plots.

Data annotation tools are software applications or platforms used to label or tag data, such as images, text, or videos, with relevant information or classifications. 

When asked about these tools, Roshan mentions that “data annotation is a challenging task, especially when datasets are complex and large, and it is very hard to ensure the quality of the annotated data when using these tools.”

Labelbox is a data annotation platform that provides tools for labeling, managing, and curating data for machine learning applications. While useful, “Labelbox requires manual effort which can be very time-consuming”.

Employing “crowdworkers” in other locations to complete specific, as-needed activities that computers can’t handle as efficiently is possible through the crowdsourcing platform Amazon Mechanical Turk. 

“Amazon Mechanical Turk is a popular choice due to its ease of use and low cost”.

Out of all the data annotation tools mentioned, Roshan underlines that Prodigy requires the most technical expertise. The tool was designed for creating training data for machine learning models.

To build, test, deploy, and manage software applications, developers rely on development platforms, which are all-inclusive environments that supply all the necessary resources. 

Google Colab is a free cloud service that offers an environment similar to Jupyter Notebooks for machine learning education and research. “We have used Google Colab for small-scale or research projects as it provides easy access to GPUs for experimentation”.

“We opted for AWS (Amazon Web Services) SageMaker for large-scale machine learning projects due to its extensive support for ML Services, and also we were more comfortable with AWS services (compared to Google services) due to our previous experiences AWS. 

One significant reason for using AWS SageMaker is that we can easily integrate it with other AWS services such as S3, EC2, and Lambda.”

APIs (Application Programming Interfaces) are sets of rules and protocols for building and interacting with software applications. AI libraries are collections of pre-written code, algorithms, and functions specifically designed to facilitate artificial intelligence and machine learning development tasks.

“In one image processing project, we had to extract features from raw images before feeding them into the model for prediction, that’s when OpenCV came in handy. Apart from feature extraction it can be used for preprocessing and manipulation of image data which can be used for object detection and image segmentation.”

Text categorisation, information extraction, question answering, and thousands of other natural language processing (NLP) activities are all part of Hugging Face Transformers, an open-source library.

“Libraries like Hugging Face Transformer have allowed us to focus on the project’s core functionality while leveraging pre-trained models for image processing tasks. The use of pre-trained models has helped us save time and resources compared to training models from scratch.”

As the realm of AI technologies continues to expand, it offers many opportunities for innovation and impact. To become an AI developer, you must master programming languages and have a solid grasp of machine learning principles.
EB Pearls stands at the forefront of web and mobile app development, integrating AI solutions to meet the sophisticated needs of our clients. Contact us today and let’s start working on your web development project together, leveraging the advantages that our extensive AI toolbox provides for our skilled developers.