There has been a significant amount of AI engineering ecosystem that has popped up in the last few years which is helping to expedite the progress in this area. Tools, frameworks, and open source libraries are making boilerplate implementation ready for use by the engineering communities. The trends are so prominent that we are seeing large-scale organizations like Google, Microsoft, and Facebook open sourcing their AI tools and framework to help the engineering community build AI-based solutions. More often than not we see that most of these tools and frameworks are in Python. The use of Python for AI has become dominant in all aspects of AI engineering work like – ML, Data Engineering, Data Science, Model Development, and Deployment as well. 

This begs the question, what is AI, and why the AI Engineering community is looking at Python as a language of choice for AI-based solutions? In the subsequent sections, let us start with building a comprehensive understanding of the Artificial Intelligence domain first, some Python-based tools that are being used, and then understand the key reasons and advantages of using Python for AI.

The AI Landscape and Benefits of using Python for AI

A Brief Journey of Artificial Intelligence (AI)

Artificial Intelligence in general, means the process of making machines mimic human behavior. Founded roughly around 1956, the idea behind Artificial Intelligence was to make machines do things that are considered to be unique human capabilities, like intelligence or intuition. In the early days, the research was mostly around board games or logic experiments. 

In the early days, in some of the cases of rule-based or expert systems, Artificial Intelligence was considered to be a glorified if-else program. This means a lot of domain knowledge of the problem was coded into the system with the help of experts from that area. Checking all possible options and then optimizing the final output. On the basis of the most plausible answer was how AI was used in the initial days.

Machine Learning: The Beginning of an Era

As the research around AI progresses, we saw the dawn of a new class of the Artificial Intelligence subdomain which we affectionately called Machine Learning. The idea was to use statistical models to learn from the data of past observations to build a model which should help explain and predict future observations. The idea of using classifiers, clustering algorithms, and other statistical techniques to make sense of the data started to take shape in academics and industry. 

Python for AI – Tools for Machine Learning 

Machine Learning which uses statistical modelling and needs to train the models with a substantial amount of data generally works with Python and R Frameworks. R is an open-source language and framework for statistical workloads. However, it is majorly preferred by the academic community, and also the library support is still catching up. Python by far is the most dominant language in this space.

The open-source community in the machine learning and statistical modelling scape is very active. Tools like NumPy, Scikit-learn, Pandas, etc. are dominantly used by engineers and scientists alike. The growing community of engineers also adds to the support that a new engineer will get while venturing into this area. 

Deep Learning: Getting Closer to Humanization of ML

Deep Learning is a subset of ML. It loosely represents the way the neurons work in our brain. The neurons are structured in layers of repetitive structures. Thus, when presented with enough data they try to learn from the data with the use of mathematical optimization techniques like gradient descent and backpropagation. 

The current state of massive availability of data and frameworks makes the training of neural networks fairly easy. It has become the tool of choice in many application areas like image processing, text processing, and trading etc.

Python for AI – Tools for Deep Learning

The Deep Learning space in the last decade has seen a massive explosion of tools pouring in from major technology giants like Google, Facebook, and Microsoft. All frameworks almost invariably support Python as the de facto language of choice for training and many for inference as well. Some of the popular frameworks also support other languages too.

Frameworks like Tensorflow, Pytorch, and MXNet are very popular frameworks used for Deep Learning engineering and experimentations. Purposely, it is maintained beginner-friendly. It also works pretty well with other frameworks for Data Processing, Engineering, and Visualization. Deep Learning engineering in general is a very repetitive and experimentation-heavy engineering process. Therefore, it needs to have a language that is flexible and is very expressive in nature. 

Data Engineering: Working with the New Oil

In the age of AI, data is the new oil and data engineering is its refining process. Artificial Intelligence has a huge dependency on data and not just any data but pretty good quality data. However, it is believed that a quality model can be build with Deep Learning. It will always depend on the type of data that we feed into the training of one. So this gives rise to another set of parallel engineering domains called Data Engineering and Data Sciences.

Data Engineering comprises data collection, data processing, data cleaning, governance, analysis, reporting, and also visualization. It deals with building tools and frameworks in place to make the whole workflow seamless and usable for various modeling and reporting tasks which can lead to better decision making. 

Python for AI- Tools for Data Engineering

The Data Engineering process requires frameworks and infrastructure to ingest, process, and store large quantities of data. This includes not just an infrastructure that scales vertically but also horizontally across large server farms. The workflow included data cleaning, feature engineering, and storing a large amount of data. 

The tools include Apache Spark, Kafka, Delta Lake, and many more. This area typically leverages a lot on the existing big data architecture. It also needs to have a very flexible infrastructure in place to play around with data in short iterative cycles. The proliferation of managed data and analytics frameworks is also very commonplace like data bricks.

Data Visualization: Connecting Numbers to Narratives

Journalist and Designer, David McCandless, in his TED talk said, “By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you’re lost in information, an information map is kind of useful.”

A picture is worth a thousand words and it is not just the numbers but the narrative behind those numbers. That need to be in place for the decision-makers to zero in on the right options. Data Visualization is quite a complex engineering piece that stands at the confluence of art and engineering.

Visualization adds narrative to the numbers and it is very meaningful when conveying the right inputs to the decision-makers in the company. It is easy to process in a condensed form. The verbose nature of the data, in general, is not very expressive to create reports and help decision-makers with the right information they are looking for. Visualization helps to bridge that gap.

Python for AI- Tools for Data Visualization

The Data Visualization tools that are available in python are Matplotlib, Seaborn, Plotly, ggplot, and Altair, etc. The visualization tools need to be simple to use APIs, cross-platform support like browser, etc. It might be helpful if it is interactive in nature. 

Why is Python the Most Preferred Language for AI?

Guido Van Rossum created Python in 1980. Since then because of its simplicity, expressiveness, and flexibility. It has been the language of choice for many general purpose applications for amateur and seasoned programmers alike. A few of the features of Python which plays out to its advantage are:

1. Simple to Learn and Use

It is simple to start and use, the developer can build expertise in this language almost effortlessly. There is a huge buffet of online tutorials that make learning Python extremely easy for beginners. Simple syntax, expressive style, and natural language semantics make it an ideal choice for developers working on AI. Further, doing quick experiments and iterations with the language is very easy because of its interpreted execution format. It can tuned to run extensively fast with the compiled version also available.

2. Mature and Supportive Community 

Python has been around for almost 30 years now and over the years it’s developer community has grown many folds. From documentation to tutorials to books there is an extensive choice of options for taking the skill levels from the beginners level to the expert in a less span of time. Getting help at the time of need builds the confidence in the programmers to dive in. It also means a lot of time saved from reinventing the wheel. 

3. Support from Large Companies 

Python among the current generation of languages possible has the biggest large-scale corporation support among its peers. With Facebook, Amazon, Google, Uber, or in-short almost the whole world is delivering their open-source frameworks and packages in Python. It is invariably becoming the default standard for developers across the globe. 

4. Versatile Open-Source Library Support

Pretty much any domain that we can think of will be very likely to have python libraries and frameworks available for the developers. It saves time, promotes reuse, and also helps to build the community of developers. 

5. Efficient, Reliable, Flexible, and Versatile

Python applications is available everywhere, whether it desktops, servers, or mobile applications. It is by far the most versatile language among the current generation of languages out there. However, the versatility of the language attracts many applications and more developers get added. Big Data, Machine Learning, and Deep Learning are some of the latest areas where Python is finding its application too.

Python has a prominent place in the Data Analytics space. The research community is in love with python and that is evident from its applications. Thousands of Machine Learning libraries are doing round and many more are getting added on a daily basis. 

6. Rapid Automation Prototyping

Python is the poster child for the automation domain. With many tools, libraries, and frameworks in place getting into automation and also mastering the art is relatively easy. In the space of Artificial Intelligence and Data Processing, a lot of automation is required largely. Due to the fact that there is a lot of data to crunch and it is just not possible to handle all this sheer volume without automation. 

Wrapping Up 

Python, with its simplicity, robustness, and expressive nature along with interpreted execution with huge open-source, corporate and community support is just the right mix of everything. Therefore, Artificial Intelligence Engineering is a highly iterative and experimentation-heavy domain. Thus Python is the perfect language to support its applications. No wonder the raging popularity of using Python for AI has only seen an upward trend and will continue to rise in the coming years as well.

We have a huge pool of expert Python Engineers with expertise in all verticals of the Artificial Intelligence domain to help your next big idea. Connect with us.

The post Python for AI: Tools and Key Advantages appeared first on EngineerBabu Blog.

I know it sounds like a bit impossible, but that’s true! Do you want to know how our Python Developers did this? Read this curated article.

Python is a general-purpose programming language that can be used for nearly everything. Python can also be used to process text, display numbers or images, solve scientific equations, and save data. NASA uses Python when they are programming their equipment and space machinery.

Clients Requirements: Our Client was from Singapore; he wanted to build a Travel app with an idea to reduce 70% travel cost and get the best memories using this excellent travel app. The app has to build for iOS as wells as for Android users. With the same approach, the client wants a two-phased application, one for the Admin side and another for the Users with distinguished feature list.

Estimated Time: What generally happens is when you estimate a timeline to the clients, it depends on various factors like complexity of the project, resources, code reusability, testing, etc. but things are not so usual here. The time to complete the project was less. The client asks us to make the Travel app within 1.5 months under the strict frame of budget.

How did the PM’s approach fall on the point?

The Project Manager (PM) is responsible for knowing the “who, what, where, when, and why” of the software project. The software development process required a ton of documentation upfront before any coding was done. The project Manager first wrote a business requirements document that captured everything the business needed in the application.

Being an established IT company, the Project Manager of EngineerBabu knows his responsibility. Since freezing the requirement doc to the end delivery of the project, the project manager handled everything swiftly.

Efforts of Python Developers

The Python Developers are responsible for building the deliverables and communicating the status of the software project to the Technical Lead and Project Manager.

Also Read: Hire Dedicated NodeJS Developer

It is critical that the other team members effectively communicate the technical requirements to the Software Developers to reduce project risk and provide the software project with the highest chance of success.

Our Python Developers took the whole responsibilities over themselves and started working on the very first day of the project dedicatedly. Our Python Developers are well experienced, and they have easily managed to drag out the project from sinking.

Counting the single Minute

Initially, it seems natural for us to accomplish the project within the given, but as the projects grow, we encountered several complexities, including designing, code structure, API integrations, etc. But the Python Programmers of EngineerBabu separated the whole Python web app development process from the mobile version. They started working individually in different modules, including back end and front end. See what they have done to make every minute count:

1. Break out large tasks into smaller pieces

We were facing a large project, so our step first was to break the whole process out into smaller goals. Then, break those goals down into smaller tasks. The more chances we have to feel like we “finished” part of it, the more motivation we got from our progress.

2. Use RescueTime to track the progress

One of the hardest parts of tracking progress is actively tracking it. Rescue Time worked in the background, meaning it automatically tracked the progress our Python Developers made each day.

3. Learned to Say “No”

One of the most essential and yet terrifying things we have ever done was to say ‘no.’ No to any new project, no to a commitment, no to someone’s request. Sometimes it is considered selfish, but for us, our client is our priority!

4. Used ‘Dead Time’ for Advantage

“Dead Time” is a time which wasted while someone else is working in their task. The point of utilizing wasted time is to use those random, useless moments to for advantage as we look for any errors, is everything well documented? Take some relaxation breaks, etc.

The Agile Approach and Testing used by our Python Developers

The Agile methodology and regular testing made our web app development process smoother. As with all problems, context is a primary constraint to solving this predicament. The agile approach helped us with:

• Individuals and interactions over processes and tools
• Working software over comprehensive documentation
• Customer collaboration over contract negotiation
• Responding to change over following a plan

Testing was done by both the parties we as well as by their product manager. For continuing the flow, the regular feedback and progress report has been sent to the clients by our developers so than they can find out the bugs at the very first moment. This helped our team to catch the ball within the timeframe.

Update the Project schedule and review the Critical Path

Within 3 weeks, our project was 80% completed, so we preponed the project schedule and devoted the rest of the time to work on critical and typical parts of the project before going live. The project was successfully delivered within the given time, and the client was much happier.

This was made possible only because of the Remote Team

EngineerBabu enables the facility to make your own offshore development team. We have distinct team members with distinct skills but the same approach. The awareness with new market trends, building the web app was made easy.

Yes, we have come across many challenges, assumptions, and constraints during development, but the team lifts the project and made us proud.

Have you ever heard this before?

Seriously, this was one of the best and the fastest projects our team has ever accomplished. If you also want to develop your project at the same pace hire Python Developers from EngineerBabu and get the best IT services. Currently, our Python Development team is working on its updates and maintenance.

Do you want to build a web app in Python? Talk to us or mail me at mayank@engineerbabu.com.

The post How our Python Developers delivered a Project in less than 4 Weeks? appeared first on EngineerBabu Blog.