According to a recent study, almost 27% of the advertised jobs require Python as a core skill, up from 18.5% at the beginning of the year.

Python is one of the TIOBE Index programming languages of the year. It has become the go-to language for developers working on data science and machine learning for a few years now. 

What sets Python apart is its intuitive features like libraries, high productivity, ease of learning etc. According to a recent study, almost 27% of the advertised jobs require Python as a core skill — up from 18.5% at the beginning of the year.

Last October, Python released version 3.9. The updates included improvements in Python internals, performance boosts, dictionary union operators, handy new string functions, new type operations, consistent and stable internal APIs and more.

What To Expect From 3.10

Python started work on the upcoming version, Python 3.10 pre-alpha, in May 2020. Python version 3.10 is set to be released on 4 October 2021. 

However, with the pre-alpha, the company has unveiled an early developer preview of Python 3.10.

The current alpha version of 3.10 is 3.10.0a6, which is the sixth of seven planned alpha releases. During the alpha phase, features may be added up until the start of the beta phase and, if necessary, may be modified or deleted until the release candidate phase. 

The significant new features of the Python 3.10 version are mentioned below:

Structural Pattern Matching

The pattern matching feature has been added in the form of a match statement and case statements of patterns with associated actions. Patterns usually consist of sequences, primitive data types mappings, as well as class instances. Pattern matching will help developers extract information from various complex data types, branch on the structure of data, and apply specific actions based on different data forms.

Better Error Messages In The Parser

When parsing code that contains unclosed parentheses or brackets the interpreter now includes the location of the unclosed bracket of parentheses instead of displaying SyntaxError: unexpected EOF while parsing or pointing to some incorrect location. Previous versions of the interpreter reported confusing places like the location of the syntax error but in Python3.10 a more informative error is emitted.

Parameter Specification Variables

Parameter Specification Variables feature supports forwarding the parameter types of one callable over to another callable, making it difficult to annotate function decorators. This feature proposes typing.ParamSpec and typing.Concatenate that help in specifying the type of a callable. 

Precise Line Numbers for Debugging and Other Tools

According to its developers, Python should guarantee that when tracing is turned on, “line” the tracing events are generated for all lines of code executed and only for lines of code that are executed. A side effect of ensuring correct line numbers is that some bytecodes will need to be marked as artificial and to assist such tools, a new co_lines attribute will be added that describes the mapping from bytecode to source.

Deprecate Distutils Module

Distutils is an undocumented and unmaintained collection of utilities for packaging and distributing Python packages, including the compilation of native extension modules. This feature defines a configuration format that describes a Python distribution and provides the tools to convert a directory of source code into a source distribution and binary distribution forms.

In both the version of Python 3.10 and 3.11, distutils will be formally marked as deprecated. Meaning, all the known issues will be closed at this time, and import distutils will raise a deprecation warning. During Python 3.10 and 3.11, uses of distutils within the standard library may change to use alternative APIs. However, in Python 3.12, distutils will no longer be installed by make install or any of the first-party distributions. 

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Artificial intelligence is not old news. According to Stanford’s 2021 AI Index, the global corporate AI investment is at a record high of $67 billion. PWCs 22nd Global CEO Survey shows that 77% of Fortune 500 CEOs are planning to or have already started AI initiatives. With the increasing importance of artificial intelligence, all eyes are on data science to deliver substantial results.

The advantage that comes with artificial intelligence for businesses is common knowledge in the tech world. The 2020 McKinsey Global AI Survey states that AI is contributing more than 20% of EBIT (Earnings Before Interest and Tax) for an elite group of artificial intelligence practitioners. Additionally, these companies have bigger budgets to spend on artificial intelligence initiatives than their competitors. With this comes the ability to develop AI solutions that they require in-house, instead of depending on external suppliers.

As data science advances, the pressure for the field to deliver tangible results and live up to its hype increases. A significant yet understated role for the success of artificial intelligence products in the AI Product Owner (AI PO). Here’s everything you need to know about AI PO.

Who Is A Product Owner?

Scrum framework, a popular agile development method defines the role of a product owner as someone who is responsible for maximizing the value of the scrum team. The scrum framework depends on the role of a product owner, scrum master, and developer. By this, we understand that a product owner creates a product vision, communicates the vision with the stakeholders, and creates the product backlog.  The Scrum framework also dictates that a PO is required to have a business, user experience, technical and communication skills.

An extended, specialized role of a product owner is the role of an AI PO. AI POs inherit the duties of a general PO with the difference that the AI POs’ work revolves around artificial intelligence-based products.

The Job Of An AI Product Owner

AI-powered products obviously differ from conventional software products. AI products use data to learn patterns without developer support. Unlike traditional software products, AI products improve on their own as and when the data keeps coming. Machine learning facilitates the building of products that were not possible to build before the emergence of AI like speech recognition devices, automating driving, etc. Because AI products hold such immense power, it is crucial for AI PO’s to adjust their skills to adapt themselves.

All The Necessary Skills

Firstly, all AI PO’s need to know everything about AI-based products, from how they work, their merits, and then pitfalls. Secondly, AI POs need to attentively monitor the predictions made by the AI models. Artificial intelligence is based on statistical assumptions, so their predictions need to be observed for uncertainty. AI POs should make it to a point to design AI applications to include human-decision making wherever necessary because one wrong prediction can have grave consequences. AI-based products are also dynamic. They identify how customers react to the prediction. This is how the system knows to make adjustments to data. Lastly, AI PO’s need to understand that AI development differs from software development and its workflow. While traditional software development can follow a modular and structural approach, AI developments test various hypotheses and perform quickly. Understanding that machine learning development isn’t as gradual as traditional software development is important to communicate the expectations with stakeholders.

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