Looking for powerful technology for your new project? The right framework makes application development simpler and faster. Your choice of tech stack can also influence the price and future possibilities of improving your business solutions.

You have heard about Python and you think it might be the best language to start your new app with? Here you’ll find some information about the most popular Python-based frameworks.

How to choose the right framework for your project

Surely, you know by now that there are plenty of technologies that could be used for web and mobile app development, creating advanced solutions for your company management and leveraging the newest methods of running a business. Python is a high-level general-purpose programming language, and it has many frameworks designed to overcome different development problems. It can be used in various projects.

But how do you decide which framework is the best for your company? Before you meet with consultants and developers from a software house, you should answer the following questions:

1. How big and complex will your desired business solution be?
2. Does your in-house team have a lot of experience in IT projects, and you’d like to choose your application structure and libraries, or would you rather the developers select it for you?
3. What quality of performance do you expect?
4. What is your preferred time-to-market?
5. What kind of special features do you expect your application will have?

You don’t have to be an expert on IT solutions for business and choose the only framework by yourself. Idego Groupconsultants have many years of experience in analyzing the business needs of small, medium, and large companies and suggesting the right solutions. All you have to do is to tell them what you require and ask about some tech stack that interests you – keep reading to learn about popular Python-based frameworks.

Flask

This is among the most popular Python-based frameworks in 2020. It provides a core set of functionalities: request handling, routing, WSGI compliance, templating, and flexibility. Using it, developers can make their own choice for matters like storage, database interaction, authentication, authorization, security solutions, and many others – as it allows for easy integration of your chosen solutions. The possibility to do so lets you keep your app free of libraries and pieces of code representing functionalities that your business solution doesn’t use at all. Apart from flexibility, you’ll benefit from scalability as Flask enables developers to scale applications easily.

Which projects should it be considered for?

• Web application development
• Building microservices
• MVP development in a short time
• Medium and large scale projects

Pyramid

This full-stack framework provides routing, renderers, and command-line tools for bootstrapping a project and enables the user to choose the libraries, database layer, and templating system. Developers can use a nice, large set of useful plugins for their projects. This may be a good choice if you expect to integrate your application with systems that would not be integrated easily with frameworks such as Django. By developing in Pyramid, developers can code with a minimalistic approach (it is similar to Flask in that regard). There is no need to deal with components that are not of high business value for your company. This results in better performance.

Which projects should it be considered for?

• Large applications
• For exposing existing Python code as a REST API
• Providing the core for web projects to be developed further

Django

Another full-stack, this Python-based framework is known as a great technology for building complex web applications. Often called “the web framework for perfectionists with deadlines”, Django is great for building advanced business products which need to be created in the shortest possible time. It comes with an automatically generated admin interface that makes quick development easier. Furthermore, it offers its object-relational mapper (ORM) for handling databases and standard solutions for authentication and authorization.

Which projects should it be considered for?

• Rapid development
• Small and large projects
• For web applications that will have to deal with high traffic
• For solutions that will contain a lot of content
• For developing API-only backend applications

Choosing the best technology is not as easy as it might seem. Always remember to analyze all your business needs carefully before making a choice and consult with experts.

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OpenCV.org has announced that the popular and long-running package OpenCV-Python is now an official OpenCV project.

OpenCV is a computer vision library that includes modules for standard image processing tasks such as filtering, warping, and color space conversion.

OpenCV was started at Intel in 1999. Its algorithms cover computer vision and machine learning and the collection is expanding day by day. OpenCV supports a variety of programming languages including C++, Python, and Java, and is available on platforms including Windows, Linux, OS X, Android, and iOS. Interfaces for high-speed GPU operations based on CUDA and OpenCL are also under active development.

OpenCV-Python is the Python API for OpenCV, combining the best qualities of the OpenCV C++ API and the Python language. It is a Python wrapper for the original OpenCV C++ implementation. It contains pre-built OpenCV with dependencies and Python bindings, so there’s no need to install OpenCV separately. OpenCV-Python makes use of the Numpy numerical operations library, and all the OpenCV array structures are converted to and from Numpy arrays. This makes it easier to integrate with other libraries that use Numpy such as SciPy and Matplotlib.

PyPi packages are provided for Windows, Linux (x86_64 and aarch64) and MacOS (x86_64). The OpenCV team is working on bringing support to the new Apple M1-powered hardware too, but say that this support depends on CI services support and hardware availability.

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When Artificial Intelligence initiatives fall short, the blame is often placed on a skills gap. But there’s more to it. Does your organization prioritize these three foundational AI pillars?

Hiring the right technical talent remains a significant roadblock to Artificial Intelligence (AI) adoption for enterprise organizations. According to a recent O’Reilly survey, slightly more than one-sixth of respondents cited difficulty in hiring and retaining professionals with AI skills as a significant barrier to AI adoption in their organizations.

While the talent gap remains a large part of the dialog, this number has decreased from the previous year, signaling that other challenges are becoming top of mind for businesses exploring and deploying AI projects.

Still, the technical skills gap isn’t the biggest impediment to AI adoption, nor is it the reason so many AI projects fail. In fact, according to the same O’Reilly survey, respondents identified a lack of institutional support as the biggest problem, followed by difficulties in identifying appropriate business use cases.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

Of course, this is a harder pill to swallow: It means the real challenge lies with us rather than with a limited number of professionals equipped to do the job.

3 pillars of AI project success

So how can organizations avoid the common pitfalls of AI projects? As with other technology implementations, it all comes down to proper company-wide training, the production environment, and having the right foundation in place. With these three pillars in place, you can start realizing the business value of AI earlier.

1. The right foundation

Successful AI projects require three things:

• Data scientists must be productively tooled, have domain expertise, and access to relevant data. While AI technology is becoming well-understood—from handling bias prevention, explainability, concept drift, and similar requirements—many teams still fall short here.
• Organizations must learn how to deploy and operate AI models in production. This requires DevOps, SecOps, and newly emerging AIOps tools and processes to be put in place so models continue working accurately in production over time.
• Product managers and business leaders must be involved from the beginning, in order to redesign new technical capabilities and decide how they will be applied to make customers happy.

While education and tooling have improved significantly over the last several years, there’s still much room for improvement in actually operating AI models in production. In that vein, product management and user interaction design are becoming common hurdles in AI success.

These problems can be addressed by investing in hands-on education. Outside the classroom and conference halls, professionals from all across your organization must get experience actually working on AI projects, understanding what they can do and how the technology can push your business forward.

2. Company-wide collaboration and training

Certainly, talent is part of the problem, but it’s not just data science talent that’s needed. The root of the problem usually lies within business and product expertise. As important as technical talent is, understanding how AI will work within a product and how it translates to better customer experience and new revenue is just as critical – and that responsibility doesn’t fall solely on the R&D team.

For example, we have algorithms that can read X-rays as accurately as humans, but we’re just now beginning to integrate this capability into the clinical workflow. If doctors and nurses aren’t trained on how to use this technology to streamline their workflow, it holds no value for them or their patients.

Being able to train and deploy accurate AI models doesn’t address the question of how to most effectively use them to help your customers. Doing this requires educating all organizational disciplines – sales, marketing, product, design, legal, customer success, finance – on why the technology is useful and how it will impact their job function.

Done well, new AI-enabled capabilities empower product teams to completely rethink the user experience. It’s the difference between Netflix or Spotify adding recommendations as a side feature versus designing their user interface around content discovery. It makes a big difference, but it also takes a village to achieve. That’s why company-wide buy-in spearheaded by the executive team is vital to AI success.

3. Proper production environment

Not all production environments are the same, so not all outcomes will be the same. It’s important to understand the limitations of AI projects based on the talent, infrastructure, and data you have and to set clear expectations from the get-go.

For example, a recent research paper (done for the ACM Conference on Human Factors in Computing Systems (CHI) series of academic conferences) explored a new deep-learning model used to detect diabetic retinopathy from images of patients’ eyes. Scientists trained a deep-learning model to identify early stages of diabetic retinopathy in patients from pictures of corneas from eye exams over the past several years. The goal was to reduce blindness, a symptom of the disease when left untreated.

The paper describes what happened when the same accurate, effective model was used in clinics in rural Thailand: The machines used to take images of patients’ eyes were not as sophisticated as the ones used for training the model. The exam rooms used were not completely dark, as the trained model assumed. For some patients, taking another day off for follow-ups or additional testing wasn’t a viable option. To boot, not all doctors and nurses were trained to explain why this new test was necessary.

The lack of proper infrastructure and cohesive education for hospital staff, coupled with an understanding of practical limitations, is a prime example of why AI projects fail.

The AI talent gap will remain a challenge for the next few years, as education catches up to industry. But in the meantime, there are steps organizations can take to ensure their AI projects prevail.

It’s not enough to just train your models – train your organization, too. Take the time to educate every facet of your business on why you’re tackling a certain AI project, how it will impact their role and the customer experience, and what the expectations are.

The right talent will come – will your organization be ready to use it?

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Machine learning is the study of computer algorithms which adapt and improve through experience, and a form of artificial intelligence.

The University’s role is to recruit recent graduates with the skills required by the company to undertake the project and ensure that Aquis remains a specialist provider of innovative technology to the finance sector.

The partnership is backed by a GBP151,000 government grant delivered by Innovate UK, which enables businesses to use KTPs to improve productivity and performance by providing the funding to take forward new ideas.

Jackie Edwards, Knowledge Exchange Manager at the University of Derby, said: “We are delighted to have secured this partnership with Aquis says, and to be supporting the company’s objective of establishing itself as one of the leading suppliers of market trading surveillance.”

Alasdair Haynes, CEO of Aquis Exchange, adds: “We are very excited to be working with the University of Derby to develop further our ML and AI capabilities for surveillance. We firmly believe that this is where the future of surveillance lies and our philosophy at Aquis is to be always at the cutting edge of innovation.”

The University has worked on KTPs with companies in a variety of different industries, from engineering to tourism, including one to measure the economic impact of Chatsworth House, the Derbyshire home of the Duke of Devonshire.

Edwards adds: “KTPs have been a very successful means of collaboration between the University and business, not-for-profit organisations and the public sector. Whether it is developing new products or improving management practices, our task is to identify and recruit graduates with the skills and knowledge to make a real difference to the employers they will work for.

“As well as bringing that expertise with them, it is also crucial that they embed that knowledge within the organisation they are working with, so it can build on the work that the graduates have carried out during their time with the company.”

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The Sonoma Valley Vintners & Growers Alliance (SVVGA) announced at its annual State of the Alliance meeting last month that it will launch a partnership with an online data-mining platform that allows wineries to better understand their own numbers by comparison with their peers.

Founded by John Keleher, the Community Benchmark platform uses proprietary algorithms to measure the relative success of tasting rooms within a geographic area and, through a private website, anonymously shares personalized, actionable metrics. The service will be available to all vintner members of the organization. Additionally, SVVGA will receive a regional subscription and gain insights and comparative analysis on the Sonoma Valley region compared to other wine regions in the United States, to unlock insights to new growth strategies.

“One of our themes for 2020 is business intelligence,” said Maureen Cottingham, executive director of SVVGA, in a press release announcing the initiative. “The majority of our vintner members stated their number one priority to their business was driving qualified visitation to their tasting rooms. By partnering with Community Benchmark, Sonoma Valley wineries will have the data to better identify their blind spots and in turn run their businesses more effectively which will result in positive growth.”

Community Benchmark provides wineries with a “refined layer of pooled data to give you exclusive insight into your business,” according to Keleher.

More than 150 vintner, grape grower, and business associate members of SVVGA gathered at the Fairmont Sonoma Mission Inn & Spa in February to learn the major initiatives and partnerships the organization will roll out in 2020.

The Sonoma Valley Vintners & Growers Alliance is a nonprofit organization serving over 500 members. Sonoma Valley comprises six of the 17 American Viticulture Areas (AVAs) of Sonoma County: Bennett Valley, Los Carneros, Moon Mountain District, Sonoma Coast, Sonoma Mountain and Sonoma Valley.

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Artificial intelligence and machine learning are crucial to Google and its parent company Alphabet. Recently promoted Alphabet CEO Sundar Pichai has been talking about an “AI-first world” since 2016, and the company uses the technology across many of its businesses, from search advertising to self-driving cars.

But regulators are expressing concern about the growing power and lack of understanding about how AI works and what it can do. The European Union is exploring new AI regulation, including a possible temporary ban on the use of facial recognition in public, and New York Rep. Carolyn Maloney, who chairs the House Oversight and Reform Committee, recently suggested that AI regulation could be on the way in the U.S., too. Pichai recently called for “clear-eyed” AI regulation amid a rise in fake videos and abuse of facial recognition technology.

Against this backdrop, the company held an event Tuesday to showcase the positive side of AI by showing some of the long-term projects the company is working on.

“Right now, one of the problems in machine learning is we tend to tackle each problem separately,” said Jeff Dean, head of Google AI, at Google’s San Francisco offices Tuesday. “These long arcs of research are really important to pick fundamental important problems and continue to make progress on them.”

While most of Google’s projects are still years out from broad use, Dean said they are important in moving Google products along.

Here’s a sampling of some of the company’s more speculative and long-term AI projects:

Robotic kittens

Google’s D’Kitty is a four-legged robot that the company says learned to walk on its own by studying locomotion and using machine learning techniques. Dean said he hopes Google’s research and development findings will contribute to machines learning how physical hardware can function in “the real world.”

Wearable sensors

Using braided electronics in soft materials, Google’s artificial intelligence technology can connect gestures with media controls. One prototype showed sweatshirt drawstrings that could be twisted to adjust music volume. The user could pinch the drawstrings to play or pause connected music.

Real-time translations

A new transcription feature in Google Translate will convert speech to written transcript and will be available on Android phones at some point in the future. Natural language processing, which is a subset of artificial intelligence, is “of particular interest” to the company, Dean said.

Google Translate currently supports 59 languages.

Detecting eye diseases

Google Health announced new research Tuesday, showing that when the company’s AI is applied to retinal scans, it can help determine if a patient is anemic. It can also detect diabetic eye diseases and glaucoma, Dean said. The company hopes to analyze other diseases in the future.

Tracking endangered species

Google is using sensing tools to track underwater sea life. Using sound detection and artificial intelligence, the company said it can now detect orcas in real time and send messages to harbor managers to help them protect the endangered species.

Google announced Tuesday that it’s teaming up with organization DFO and Rainforest Connection to track critically endangered Southern Resident killer whales in Canada. The company’s also in the early stages of working with the Monterey Bay Aquarium to help detect species in the ocean nearby.

Sign language detection

Google’s working on a project called MediaPipe, which analyzes video of bodily movements including hand tracking. Dean said the company hopes to read and analyze sign language.

“Video is the next logical frontier for a lot of this work” Dean said.

All in all, the day resembled a science fair more than anything else, but it helped make Google’s point that artificial intelligence can have useful real-world applications.

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Waymo, Google’s self-driving car sibling company, has acquired the Oxford artificial intelligence company Latent Logic for an undisclosed amount, giving Waymo its first presence in the UK.

Latent Logic, a spinout company from Oxford University, specialises in “imitation learning”, teaching machines how to act by showing them examples of humans doing the same actions. It was founded in 2017 by the academics Shimon Whiteson and João Messias.

Waymo hopes the company’s expertise can be put to use teaching AI drivers how to deal with complex behaviour such as a car cutting off another at a roundabout, a pedestrian emerging from a parked car, or a cyclist skidding in rain.

“The team’s expertise in reinforcement learning and imitation learning can help further accelerate Waymo’s progress in areas from simulation to behaviour prediction and planning,” the company said in a statement.

The Alphabet subsidiary is also intending to use its new base in Oxford to build a second pool of AI talent outside its headquarters in Mountain View, California. The UK is a world leader in AI research, including autonomous vehicles, and many talented researchers will not or cannot relocate to the US – no matter how deep the recruiter’s pockets.

Drago Anguelov, Waymo’s principal scientist and head of research, said: “We see an exciting opportunity in Europe, not only in continuing to build our partnerships with major automakers but also in benefitting from the world-class technology and engineering capabilities in Oxford and beyond.”

Whiteson, who will continue to work as a professor of computer science at Oxford, added: “By joining Waymo, we are taking a big leap towards realising our ambition of safe, self-driving vehicles. In just two years, we have made significant progress in using imitation learning to simulate real human behaviours on the road. I’m excited by what we can now achieve in combining this expertise with the talent, resources and progress Waymo have already made in self-driving technology.”

The acquisition is about research only, however: for the time being, Waymo has no plans to launch its self-driving taxis in the UK. Those remain strictly active only in Phoenix, Arizona, where they have recently begun to operate in a fully driverless manner, ferrying paying passengers around the city without even a human safety driver in the front seat.

The company does plan to operate in Europe in the near future, however, working with Renault to develop a self-driving taxi service for the 2024 Paris Olympics, when it hopes to be able to shuttle travellers from Charles de Gaulle airport to La Défense.

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As part of new efforts toward accessibility, Google announced Project Euphoniaat I/O in May: An attempt to make speech recognition capable of understanding people with non-standard speaking voices or impediments. The company has just published a post and its paper explaining some of the AI work enabling the new capability.

The problem is simple to observe: The speaking voices of those with motor impairments, such as those produced by degenerative diseases like amyotrophic lateral sclerosis (ALS), simply are not understood by existing natural language processing systems.

You can see it in action in the following video of Google research scientist Dimitri Kanevsky, who himself has impaired speech, attempting to interact with one of the company’s own products (and eventually doing so with the help of related work Parrotron): The research team describes it as following:

It’s notable that they at least partly blame the training set. That’s one of those implicit biases we find in AI models that can lead to high error rates in other places, like facial recognition or even noticing that a person is present. While failing to include major groups like people with dark skin isn’t a mistake comparable in scale to building a system not inclusive of those with impacted speech, they can both be addressed by more inclusive source data.

For Google’s researchers, that meant collecting dozens of hours of spoken audio from people with ALS. As you might expect, each person is affected differently by their condition, so accommodating the effects of the disease is not the same process as accommodating, say, a merely uncommon accent.

A standard voice-recognition model was used as a baseline, then tweaked in a few experimental ways, training it on the new audio. This alone reduced word error rates drastically, and did so with relatively little change to the original model, meaning there’s less need for heavy computation when adjusting to a new voice.

The researchers found that the model, when it is still confused by a given phoneme (that’s an individual speech sound like an “e” or “f”), has two kinds of errors. First, there’s the fact that it doesn’t recognize the phoneme for what was intended, and thus is not recognizing the word. And second, the model has to guess which phoneme the speaker did intend, and might choose the wrong one in cases where two or more words sound roughly similar.

The second error in particular is one that can be handled intelligently. Perhaps you say, “I’m going back inside the house,” and the system fails to recognize the “b” in back and the “h” in house; it’s not equally likely that you intended to say “I’m going tack inside the mouse.” The AI system may be able to use what it knows of human language — and of your own voice or the context in which you’re speaking — to fill in the gaps intelligently.

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Despite pledging to cut ties with weapons-based artificial intelligence (AI)projects, Google is using its investment arm to cultivate startup firms that actively engage in military and law enforcement contracts.

The industry giant’s involvement in a highly contentious AI-powered Pentagon drones project last year, dubbed Project Maven, garnered anger and protestations from its own employees.

Following a messy public dispute, Google published an ethical code of conduct and declared it would pull out Project Maven, as well as work on any direct AI military applications. The firm even withdrew from a $10 billion Pentagon cloud project in October because it may conflict with its “corporate values”.

Whistleblowers, however, have claimed Google is circumventing its own guidelines by providing funds and guidance to AI startups via its investment arm Gradient Ventures, according to The Intercept. This is a financial fund created by Google specifically to invest in AI startups.

Not only do these companies receive financial support, Google employees said, but they are also granted access to Google’s vast repository of training data accumulated through work with its own AI systems.

Moreover, these startups will receive advanced AI training on behalf of Google. Emails also suggest senior Google engineers will take up roles in these startups in order to offer “the kind of hand-holding support that we think is helpful in growing an AI ecosystem”.

When the company published its code of ethics, critics suggested the exact terms left the door open to continued involvement with the military and law enforcement in other areas such as recruitment and training.

Companies supported by Gradient Ventures, however, are directly involved in supplementing weapons systems with AI technology. Cogniac, for example, provides image-processing software to the US army in order to assess battlefield drone data.

CAPE productions, meanwhile, provides law enforcement with AI-powered software to guide fleets of drones to conduct aerial surveillance.

Google isn’t the only major tech company to sustain heavy criticism for its involvement with law enforcement. Amazon, for instance, has been savaged by immigration rights groups for its work on behalf of the US Immigration and Customs Enforcement agency (ICE).

IT Pro asked a Google spokesperson to explain how its investment activities were consistent with its own ethical guidelines.

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Two consecutive budgets read by two Finance Ministers (Arun Jaitley and Piyush Goyal) had provisions for a National Artificial Intelligence Portal. But, almost two years have passed since AI (artificial intelligence) was given prominence.

It looks like the project it stuck in red tape.

Both the Ministry of Electronics and Information Technology (MeitY) and Niti Aayog are now staking claim to the project, leading to a complete standstill.

Presenting his 2018-2019 budget speech Arun Jaitley had mandated the Niti Aayog to establish the national programme on AI to guide research and development in new and emerging technologies.

Almost a year later, presenting the Interim Budget 2019-20 in the Lok Sabha, interim Finance Minister Piyush Goyal had spoken about nine priority areas that have been identified for the development of the AI sector in the country.

Even Finance Minister, Nirmala Sitharaman in her maiden speech had said that the government will be increasing its efforts to improve the skills of youth in fields of Artificial Intelligence (AI), big data, robotics, and other newer skills.

Provisions for AI are galore but the project is stuck in a limbo because both Niti Aayog and MeitY are wanting to take the project forward. Sources within the IT Ministry have told India Today that a budget of 300 crores has been sent to the department of expenditure under that Finance Ministry.

Interestingly, even Niti Aayog (government’s think tank) has submitted an expenditure report of approximately Rs 7,000 crore to the department of expenditure.

The department of expenditure is now contemplating the next course of action with regards to the varied budgets it has received.

Prime Minister Narendra Modi has been vocal about his desire to expand India’s reach in artificial intelligence sphere.

On one hand the project is stuck because both MeitY and Niti Aayog are staking a claim on it and on the other hand the government is making tall claims on back of the subject.

Responding to supplementaries during Question Hour, Human Resource Development Minister Ramesh Pokhriyal Nishank said that engineering students are being trained so that they can be a part of the ‘Make in India’ initiative of the government.

The minister highlighted that the All India Council for Technical Education (AICTE) will not allow new conventional disciplines with low employment potential from the academic year 2020-21 and will permit only emerging fields like artificial intelligence and blockchain.

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