Category Archives: Programming languages


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As the world’s leading Internet television network with over 160 million members in over 190 countries, our members enjoy hundreds of millions of hours of content per day, including original series, documentaries and feature films. Of course, all our all-time favourites are right on our hands, and that is where machine learning has taken it’s berth on the podium. This is where we will dive into Machine Learning.


Machine learning impacts many exciting areas throughout our company. Historically, personalization has been the most well-known area, where machine learning powers our recommendation algorithms. We’re also using machine learning to help shape our catalogue of movies and TV shows by learning characteristics that make content successful. Machine Learning also enables us by giving the freedom to optimize video and audio encoding, adaptive bitrate selection, and our in-house Content Delivery Network.

I believe that using machine learning as a whole can open up a lot of perspectives in our lives, where we need to push forward the state-of-the-art. This means coming up with new ideas and testing them out, be it new models and algorithms or improvements to existing ones.

Operating a large-scale recommendation system is a complex undertaking: it requires high availability and throughput, involves many services and teams, and the environment of the recommender system changes every second. In this we will introduce RecSysOps a set of best practices and lessons that we learned while operating large-scale recommendation systems at Netflix. These practices helped us to keep our system healthy:

 1) reducing our firefighting time, 2) focusing on innovations and 3) building trust with our stakeholders.

RecSysOps has four key components: issue detection, issue prediction, issue diagnosis and issue resolution.

Within the four components of RecSysOps, issue detection is the most critical one because it triggers the rest of steps. Lacking a good issue detection setup is like driving a car with your eyes closed.


The very first step is to incorporate all the known best practices from related disciplines, as creating recommendation systems includes procedures like software engineering and machine learning, this includes all DevOps and MLOps practices such as unit testing, integration testing, continuous integration, checks on data volume and checks on model metrics.

The second step is to monitor the system end-to-end from your perspective. In a large-scale recommendation system there are many teams that often are involved and from the perspective of an ML team we have both upstream teams (who provide data) and downstream teams (who consume the model).

The third step for getting a comprehensive coverage is to understand your stakeholders’ concerns. The best way to increase the coverage of the issue detection component. In the context of our recommender systems, they have two major perspectives: our members and items.

Detecting production issues quickly is great but it is even better if we can predict those issues and fix them before they are in production. For example, proper cold-starting of an item (e.g. a new movie, show, or game) is important at Netflix because each item only launches once, just like Zara, after the demand is gone then a new product launches.

Once an issue is identified with either one of detection or prediction models, next phase is to find the root cause. The first step in this process is to reproduce the issue in isolation. The next step after reproducing the issue is to figure out if the issue is related to inputs of the ML model or the model itself. Once the root cause of an issue is identified, the next step is to fix the issue. This part is similar to typical software engineering: we can have a short-term hotfix or a long-term solution. Beyond fixing the issue another phase of issue resolution is improving RecSysOps itself. Finally, it is important to make RecSysOps as frictionless as possible. This makes the operations smooth and the system more reliable.


To conclude In this blog post I introduced RecSysOps with a set of best practices and lessons that we’ve learned at Netflix. I think these patterns are useful to consider for anyone operating a real-world recommendation system to keep it performing well and improve it over time. Overall, putting these aspects together has helped us significantly reduce issues, increased trust with our stakeholders, and allowed us to focus on innovation.




[1] Eric Breck, Shanqing Cai, Eric Nielsen, Michael Salib, and D. Sculley. 2017. The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction. In Proceedings of IEEE Big Data.Google Scholar

[2] Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett(Eds.). Curran Associates, Inc., 4765–4774.

When life gives you data, make an analysis!

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… or a day in the life of a data analyst 

Data analysts, who are entrusted with converting raw data into insights that can be used to assist decision-making, are the unsung heroes of the data industry. Large amounts of data need to be gathered, cleaned and analyzed as part of their profession in order to find trends, patterns, and linkages that could otherwise go missed. In a time when data is king, data analysts are essential in assisting businesses in making sense of the massive volumes of data they produce every day.

A data analyst’s day can be varied and difficult, involving everything from gathering and cleaning data to examining and visualizing it to developing and testing predictive models. In addition to having a solid grasp of statistics, data visualization, and machine learning, data analysts must be able to concisely and clearly convey their findings to stakeholders. In order to comprehend the business context of the data and guarantee that their research meets the objectives of the stakeholders, they must also be able to work collaboratively with cross-functional teams that include engineers, product managers, and business analysts.

One of the most important tools in the data analyst’s arsenal is the programming language Python, which has become the de facto language for data analysis and data science. Python offers a wealth of libraries and tools that make it easy to perform data analysis tasks, such as collecting data, cleaning data, exploring data, and building predictive models.

Python, a programming language that has established itself as the standard for data analysis and data science, is one of the most crucial weapons in the toolbox of the data analyst. Python has an abundance of modules and tools that make it simple to carry out data analysis tasks like gathering data, cleaning data, examining data, and developing predictive models.

Here are some of the most common Python libraries used for data analysis:

  • Pandas: A fast, flexible, and powerful data analysis and manipulation library, used for tasks such as data cleaning, aggregation, and transformation.
  • Numpy: A library for numerical computing in Python, used for tasks such as linear algebra, random number generation, and array operations.
  • Matplotlib: A 2D plotting library for Python, used for tasks such as data visualization, histograms, and scatter plots.
  • Seaborn: A data visualization library based on Matplotlib, used for tasks such as regression plots, heatmaps, and violin plots.
  • Scikit-learn: An open-source library for machine learning in Python, providing a wide range of algorithms for classification, regression, clustering, and dimensionality reduction.
  • TensorFlow: A popular open-source platform for developing and training ML models, used for a wide range of tasks including image recognition, natural language processing, and time series forecasting.
  • PyTorch: An open-source ML framework for building and training deep learning models, used for tasks such as image classification, sequence analysis, and recommendation systems.

To conclude, data analysts are essential to helping businesses understand the enormous amounts of data they produce every day. To transform data into insights and encourage reasoned decision-making, they combine technical abilities, like Python programming and machine learning, with soft skills, like cooperation and communication. The world of data is an exciting and gratifying place to be, and there are endless opportunities for growth and development whether you are an experienced data analyst or just getting started.


ChatGPT’s new competitor

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More powerful than ChatGPT': Microsoft unveils new AI-improved Bing and Edge  browser | ZDNET

Bing is an updated Microsoft search service based on artificial intelligence. It’s based on the OpenAI GPT language model, but Bing is newer than ChatGPT 3.5. Microsoft says it’s not just an updated search engine, but a new artificial intelligence-based search channel with a new chat interface that offers better searches, more complete answers and more relevant search results, so readers can spend less time on the page. Artificial intelligence will revolutionize every category of software, including the largest category — search. Bing can also create content and inspire creativity. Microsoft said: “The new Bing can generate useful content. Create a 5-day itinerary for your dream vacation to Hawaii, including links to write emails, book travel and accommodation, prepare for interviews and create quiz questions to help you The new Bing also cites all sources so you can see links to the web content you link to.”

Microsoft has also announced changes to Edge. Artificial intelligence has been added to Edge to help people do more with search and the internet. As for the new Bing search and the new Edge browser, Microsoft highlights some key features:

  • The best search. The new Bing offers an improved version of familiar search, providing more relevant results for simple things like sports scores, promotions and weather, as well as more complete when you need it. It also provides a new sidebar for displaying responses.
  • Full answer. Bing searches the web for results to find and summarize the answers you are looking for. For example, you can get step-by-step instructions on how to replace eggs with another ingredient in your current cake without looking at multiple results.
  • New chat. For more complex searches, such as planning a detailed travel itinerary or choosing a TV to buy, the new Bing offers a new interactive chat. Chat allows you to narrow down your search until you get the full answer you are looking for, asking for details, clarity and ideas. Links are available, so decisions can be made immediately.
  • New Microsoft Edge interface. We have updated the Edge browser with new artificial intelligence features, a new look and added two new features: chat and messaging. Use the Edge sidebar to request summaries of long financial reports to get the main conclusions, use the chat function to request comparisons with competitors’ financial reports, and automatically place them in a spreadsheet. You can also ask Edge to help you create content, such as posts for LinkedIn. Then you can get help updating the tone, format and length of your message. Edge can understand the web pages you are viewing and adapt accordingly.

However, Google issued a warning to its departments, and even the founders and shareholders of the tech giants Larry Page and Sergey Brin stepped up. On Monday, the company introduced its own alternative to ChatGPT called Bard. Google CEO Sundar Pichai called the software an “experimental artificial intelligence service” that is still being tested by a limited number of users and employees of the company and will be released to the general public in the coming weeks.

Microsoft Brings ChatGPT-Like AI Features to Bing, Edge - My TechDecisions

Thus, Bing have been developed to facilitate research and increase their reliability. Starting with the chat mode, you can ask literally any question using an interface very similar to GPT chat, and the answer will be sent in seconds.

Interestingly, when searching for information in real time on the Internet, responses are sent directly from various thematic sites. The source of information for constructing the answer is shown as a footnote, but the user is redirected to the main page of the site in question, and not to the page with the text.

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Deepmind’s AlphaCode Satisfactory in a Programming Competition

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Source: Maciek905/Dreamstime stock image

AI code generation systems are a type of artificial intelligence technology that is capable of automatically generating code. These systems have the potential to revolutionize the way software is developed, making it faster and more efficient.

One of the main benefits of AI code generation systems is their ability to save time. These systems can analyze a given problem and automatically generate a solution in the form of code. This can significantly reduce the amount of time it takes for developers to write code from scratch. Additionally, these systems can often generate code that is more efficient and optimized than code written by humans, which can lead to faster and more reliable software.

Another benefit of AI code generation systems is their ability to improve the accuracy and reliability of code. By analyzing a problem and generating a solution, these systems can help eliminate human error that can lead to bugs and other issues in software. This can help reduce the time and resources needed for debugging and testing, which can save money and improve the overall quality of the software.

One of the main challenges of AI code generation systems is their reliance on data. These systems need large amounts of data to learn and generate code, which can be a problem if the data is not available or is of poor quality. Additionally, these systems are only as good as the algorithms and models they are based on, and it can be difficult to design and train these models to generate high-quality code.

Despite these challenges, there has been significant progress in the development of AI code generation systems in recent years. One example is the development of “neural machine translation” systems, which are capable of automatically translating text from one language to another. These systems have been able to achieve impressive levels of accuracy, and they have been widely adopted in a variety of industries.

Another example is the development of “auto-coding” systems, which are capable of generating code for a variety of programming languages. These systems have the potential to significantly reduce the time and effort required to develop software, and they are being explored by a number of companies and organizations.

Examining the abilities of AI code generation systems can be tricky. One means of doing so is to place the system in a programming competition against regular human programmers. A recent experiment of that kind was performed by Deepmind. Deepmind, a subsidiary of Alphabet Inc. is a trailblazing artificial intelligence research laboratory. The experiment was carried out with the use of its AlphaCode deep learning algorithm. AlphaCode converts user input into functioning code by first rewriting it as an action plan. It transforms it into set steps and finally turns it into fully working code. AlphaCode achieved an ‘average’ rating in the competition. A promising acceleration for AI code generation systems.

Overall, AI code generation systems have the potential to revolutionize the way software is developed. These systems can save time and improve the accuracy and reliability of code, and they have already made significant progress in a number of areas. However, there are still challenges to be addressed in terms of data availability and model design, and it will be interesting to see how these systems continue to evolve and improve in the coming years.


DeepMind. “Competitive programming with AlphaCode.” Deepmind. Published December 8, 2022.

Li, Yujia et al. “Competition-level code generation with AlphaCode.” Science. Published December 8, 2022.

Kolter, J. Zico. “AlphaCode and “data-driven” programming.” Science. Published December 8, 2022.

Deepmind. “AlphaCode Attention Visualization.” Deepmind. Accessed January 9, 2023.