Java 16 and Imminent Release
JDK 16 lauch offers better performance and productivity that has imminent release of the java 17 known as JDK enhancement Proposal with main changes. Java 16 finalized the language enhancement introduced in the JDK 14. Java 16 offers the value-added memory management ,new packing tool and new UNIX – Domain Socket channels. The two features Elastic metaspace and ZGC: concurrent thread are included for vendor benefit. The enhanced features of java instance pattern matching is now avilable in java 16. The JEP 395 (JDK Enhancement Proposals) records enhance portability of classes syntax to increase code maintainability and readability. In JVM updates ZGC java thread stack processing to concurrent level for enhancing the efficicency of software application and to reduce the maintenacnce cost metaspace provide hotspot class and metadata memory.In case of Net Tools and Libraries the old concept enhanced in Unix domain socket channel. The packaging tool – packing of self contained java application providing end user installation work more comfortable. Other features like warning for value based class , strong JDK internals encapsulation, vector API, foreign linker API, Memory access API and Sealed classes are enhancements in Incubator. For improving the performance OpenJDK developers provided the C++14 name JDK C++ featuers. OpenJDK’s source code repositories migration , migrate to Githuband alpine linus port and AAch64 port are the addon features. With lauch of java 16 , Oracle cotinue to accept the best IDE’s vendors and support the future version of JDK.
September 2021 GST returns - Time to do self-assessment of books of accounts from GST Stand point of view for Financial Year 2020- 2021
Business had a challenging financial year. There was disruption to normal business due to Covid-19 pandemic. Government extended due dates for various returns, waiver of interest for late of payment and for delayed filing of returns for Small, MSMEs as well as large taxpayers. Government was extending the concessions based on rapidly changing situation. In view of the above, there is need to reconcile books with returns. Taxpayers have an opportunity to correct those errors committed unintentionally in their GST returns during financial year 2020-21.
Taxpayers needs to examine, resolve and reconcile the books with returns before filing of their monthly return for September 2021. A few areas are mentioned below, which they can review and act upon:
- GSTR1- Reconciliation of Sales register with GSTR1. In case of any difference, make appropriate corrections.
- GSTR3B - The same exercise needs to be repeated done with GSTR-3B.
- Financial books – It needs to be tallied sales uploaded in the GST portal.
- Export invoices – Ensure it is properly correlated with Customs to get refund seamlessly.
- Examine and confirm that correct reporting of NIL rated, exempted and non-taxable supplies in the GSTR-1.
- Advances from Customers – Examine the following:
- Whether if any advances received during the year?
- If yes, check if it is settled, then necessary adjustments should be carried out in books and GST portal.
- Sales/Debit notes/Credit notes of previous year (FY: 2019-20) reported in the during period from April 2020 to March 2021 needs to be deducted.
- In the same manner, Sales/Debit notes/Credit notes for the FY: 2020-21 reported after March 2021, needs to be added to arrive at the correct turnover.
- Credit notes pertaining to financial year 2020-21, which is pending to be reported in the GST portal should be done in September 2021. If not, you can only use the commercial credit note without any adjustment of GST liability.
- In case of any invoices or debit note or credit note is missed out including any corrections to invoice number, date, taxable value and total value can be done while filing the GSTR1.
Input Tax Credit (ITC)
- Examine, analysis and reconciliation of purchase register with GSTR-2A/2B availed in GST returns of FY 2020-21.
- Avail any unclaimed ITC of FY 2020-21 in September 2021 GSTR-3B.
- If any ITC is not received, co-ordinate with supplier and ensure your ITC is credited in GSTR2A/2B. In the same way, if any supplier has mis-spelt your GSTIN, request them to necessary correction.
- Check and request supplier to rectify the transactions if they had wrongly linked your invoice as B2C instead of B2B.
- Prepare list of the eligible and ineligible ITCs separately. This is required for reporting in annual return. Reconcile them with the physical copy of the invoices/debit notes. If any missing, please collect from vendors.
- Analyze ITC availed and reversed in the GST returns during the last year. If any ineligible ITC wrongly recorded in the eligible credit and vice versa, please do the necessary corrections.
- Reverse Charge Mechanism (RCM): Identify if supplies subject to RCM under GST had been correctly disclosed in GSTR-3B return
- ITC needs to be reversed, if the payment to Vendor’s invoice/debit note is not made within 180 days as per Rules 37 of CGST Rules, 2017.
- ITC reflected in GSTR-2A without corresponding invoice, please collect the same with vendor. The opposite – invoice is available with you but ITC not reflected in GSTR-2A, request vendor to file invoices details in their September 2021 GSTR-1, if not, please reverse them.
If Rule 42 and 43 is applicable to your business, then ITC should be reversed in the following manner:
- Actual reversal as per formula.
- If the actual ITC calculation is less than actual reversal, reverse the balance ITC in September 2021 GSTR-3B or pay the same through Form GST DRC-03 along with interest at the rate of 18%. The interest will be calculated from April 01, 2021 (in respect of FY 2020-21) till the date of reversal or actual payment, as the case maybe.
- If the excess ITC reversed, claim the same in September 2021.
- Check on whether correct GST rates is used along with proper HSN/SAC codes to ensure accuracy of tax liability arrived upon.
- Liability due and corresponding payments made during the year needs to be mapped correctly.
- Balance in credit registers is reconciled with financial books.
If you are able to validate the points, then you can comfortably able to file your annual returns for the financial year 2020-21.
Python is one of the most popular programming languages used in the field of machine learning. According to Kaggle’s annual survey of machine learning engineers, about 90% of respondents reported using Python in 2020.
Tech giants like Spotify, Amazon, and more rely heavily on Python to power their machine learning operations and build more effective products. Netflix uses Python to create and manage recommendation algorithms, personalization algorithms, and marketing algorithms. From robotics to machine learning, many of Google’s AI investments depend on Python as well.
How Machine Learning Engineers Use Python
Python is used to implement machine learning models and systems. In the context of AI development, Python’s simplicity is a major plus.
Its clarity and succinct structure allows machine learning engineers to focus on the content of ML problems over writing code, which speeds up development. With Python, machine learning engineers can quickly test algorithms prior to deployment. Machine learning engineers also use a variety of Python frameworks and libraries, including:
- Matplotlib and Seaborn. Machine learning engineers frequently need to execute exploratory data analysis to evaluate which algorithm to apply to a data set. These Python libraries help machine learning engineers visualize and identify trends in data.
- Pandas. Machine learning engineers use this library for data manipulation and analysis. Data fuels machine learning, and every machine learning engineer must clean, process, and transform data in order to produce high-quality insights.
- Scikit-learn. This Python package helps machine learning engineers to implement supervised and unsupervised algorithms. Scikit-learn includes classification, clustering, and regression algorithms. Machine learning engineers also use this tool to score algorithms for functionality and split modelling data into testing and training sets.
- Keras and TensorFlow. Machine learning engineers use Keras and TensorFlow to build, train, and deploy machine learning models and deep neural networks.
Machine learning engineers rely on Python’s vast library ecosystem to manage and understand their data—and to deploy AI solutions in production.